Electricity Market Framework for Renewable and …...The final model used was based upon the IEEE...

i

Electricity Market Framework for Renewable and Distributed

Generation

A thesis submitted in partial fulfilment of the

requirements for the award of the degree

Bachelor of Engineering (Electrical)

From

University of Wollongong

By

Matthew Conley

School of Electrical, Computer and

Telecommunications Engineering

October, 2013

Supervisor: Dr. Ashish Agalgaonkar

ii

Abstract

With the introduction of the Renewable Energy Target (RET) and carbon pricing scheme

there has been a large increase in the amount of renewable generation being

implemented in the National Electricity Market (NEM). Renewable generation has two

main advantages being zero fuel costs and zero emissions output. This paper

investigates the benefits, disadvantages and incentives for the introduction of

distributed and renewable generation in the NEM. In order to become a more viable

investment choice the current market framework needs to be modified to better include

distributed and renewable generation sources. By making it easier and clearer for

companies to setup new distributed and renewable generators it creates more of

incentive for the generating companies to reduce their use of traditional high emissions

power plants and invest in renewable zero emission generators.

In this study the effects of distributed and renewable generation are observed from an

electricity market perspective. The simulations compare how the Locational Marginal

Pricing (LMP) and spot price are effected under different penetration levels of

Distributed Generation (DG). This was studied through the use of the modelling software

Matlab using the Matpower toolbox and PLEXOS. These allowed for different

penetration levels of DG to be placed throughout the IEEE 14-Bus test system.

Two new test systems were then built and analysed. They included an Australian NEM

model using PLEXOS and Matlab. These test systems were used to study the effects that

DG could have in the prevention of interconnectors contingency situations, that being

the inability to serve the required load. The final model used was based upon the IEEE

118-Bus test system. It was chosen as it is representative of an electricity network of

similar size to either NSW or QLD. This model was used to verify the most beneficial

location as to where DG placement should occur. The final simulations consisted of

modelling DG up to 30% in the IEEE 118-Bus model using solar and wind renewable

generation.

iii

Acknowledgements

I would like to take this opportunity to thank my supervisor Dr Ashish Agalgaonkar for

his consistent support and guidance throughout the entire thesis project. I would like to

thank my Mum and Dad, Michelle and Brett for their consistent support and

understanding throughout the entire course of my university degree. I also thank my

entire family as you have all been very supportive. Finally I would like to thank my three

best friends Michael, Danielle and Steve for putting up with me for the last four years

and all of your constant constructive criticism, it’s nearly time for Vegas!

iv

Statement of Originality

I, Matthew Dylan Conley, declare that this thesis, submitted as part of the requirements

for the award of Bachelor of Engineering in the School of Electrical, Computer and

Telecommunications Engineering, University of Wollongong, is wholly my own work

unless otherwise referenced or acknowledged. The document has not been submitted

for qualifications or assessment at any other academic institution.

Signature:

Print Name: Matthew Dylan Conley

Student ID Number: 3883383

Date:

v

Contents

Abstract .................................................................................................................... ii

Acknowledgements .................................................................................................. iii

Statement of Originality ........................................................................................... iv

List of Figures ......................................................................................................... viii

List of Tables ............................................................................................................ ix

List of Changes ......................................................................................................... ix

Abbreviations ........................................................................................................... x

1. Introduction ...................................................................................................... 1

1.1 Thesis Objectives ................................................................................................ 2

1.2 Thesis Organisation ............................................................................................ 3

1.3 Thesis Contribution ............................................................................................ 4

2 Literature Review .............................................................................................. 5

2.1 Australian National Electricity Market ............................................................... 5

2.2 Wholesale Electricity Market ............................................................................. 5

2.3 Hourly Spot Price ................................................................................................ 6

2.4 Market Modelling and Generator Cost Functions ............................................. 7

2.4.1 Coal and Gas Based Steam Generation ...................................................... 8

2.4.2 Hydro Based Generation ............................................................................. 9

2.4.3 Wind Based Generation .............................................................................. 9

2.4.4 Solar Based Generation ............................................................................ 10

2.5 Introduction of Renewable and Distributed Generation in the NEM .............. 10

2.6 Renewable Energy Target and Emissions Trading Scheme .............................. 10

vi

2.7 Effects of Renewable and Distributed Generation on Electricity Market ....... 11

2.7.1 Market Regulation Approach in NEM ....................................................... 11

2.7.2 Distributed and Renewable Market Framework Approach used in the

Electricity Networks of other Countries .................................................................. 12

2.7.3 Reduction in Spot Price ............................................................................. 13

2.7.4 Transmission Deferment ........................................................................... 13

2.7.5 Economically Viable Microgrid ................................................................. 14

2.7.6 DG Placement in Local Distribution Network ........................................... 15

2.8 Simulation Software Packages ......................................................................... 16

2.8.1 Matpower OPF Studies ............................................................................. 16

2.8.2 Matpower and PLEXOS Comparison ......................................................... 16

3 Previous Outcomes .......................................................................................... 18

3.1 Implementation of DG at Different Busses ...................................................... 18

3.1.1 10-30% DG Implementation on Multiple Busses ...................................... 18

4 Methodology and Results ................................................................................. 20

4.1 NEM Model ...................................................................................................... 20

4.1.1 PLEXOS simulated NEM Model ................................................................. 21

4.1.2 MATLAB Simulated NEM Model ............................................................... 22

4.1.3 Contingency Situation ............................................................................... 24

4.1.4 Comparison of Models .............................................................................. 24

4.1.5 Bass Link Contingency Analysis in PLEXOS ................................................ 27

4.2 MATLAB 118-BUS DG Modelling ...................................................................... 30

4.2.1 Standard Results ....................................................................................... 31

4.2.2 Determining DG Location ......................................................................... 31

vii

4.2.3 Creating DG Penetration Data .................................................................. 33

4.2.4 Solar Penetration Modelling ..................................................................... 34

4.2.5 Solar Penetration Results.......................................................................... 35

4.2.6 Wind Penetration Modelling .................................................................... 38

4.2.7 Wind Penetration Results ......................................................................... 39

4.2.8 Comparison of Solar and Wind Penetration ............................................. 43

5 Conclusion ....................................................................................................... 45

6 Future Work ..................................................................................................... 46

7 References ....................................................................................................... 47

Appendix A.1 – Project Plan and Specifications ........................................................ 50

Appendix A.2 – Spring Session Gantt Chart .............................................................. 53

Appendix B.1 – Log Book Signature Sheet ................................................................ 54

Appendix C.1 – NEM Load Data, 1st January 2009 ..................................................... 55

Appendix C.2 –PLEXOS Generator Data ................................................................... 56

Appendix C.3 – Matlab NEM Function ..................................................................... 58

Appendix D.1 – DG Penetration Data ....................................................................... 61

Appendix D.2 – 48 Period Solar Irradiance Data, 1st January 2009 ............................ 62

Appendix D.3 – “30% Solar” Matlab Bus Data .......................................................... 63

Appendix D.4 – Wind Speed Data ............................................................................ 66

Appendix D.5 – “30% Wind” Matlab Bus Data ......................................................... 67

viii

List of Figures

Figure 1: DG Effect on Cost in 14-Bus System ................................................................ 19

Figure 2: 1st January 2009, NEM Load Data [18]............................................................ 20

Figure 3: PLEXOS NEM Model Layout ............................................................................. 21

Figure 4: MATLAB NEM Model Layout ........................................................................... 23

Figure 5: Bass Link Contingency Situation 1 ................................................................... 25

Figure 6: Bass Link Contingency Situation 2 ................................................................... 26

Figure 7: NEM Contingency Analysis .............................................................................. 27

Figure 8: Spot Price - Bass Link @480MW ...................................................................... 28

Figure 9: Spot Price - Bass Link @470MW ...................................................................... 28

Figure 10: 118-Bus Test System Topography ................................................................. 30

Figure 11: Load and LMP of Each Bus in the 118-Bus Model ......................................... 31

Figure 12: DG Effect on LMP when Placed on Most Expensive Busses .......................... 32

Figure 13: DG Effect on LMP when Placed on Most Loaded Busses .............................. 32

Figure 14: Solar Power Implementation in Matlab ........................................................ 34

Figure 15: Solar Penetration Data Loop .......................................................................... 34

Figure 16: Average and Peak LMP 24hr Prices for 10% Solar Penetration ..................... 35



Figure 19: Wind Penetration Data Loop ......................................................................... 38

Figure 20: Average and Peak LMP 24hr Prices for 30% Wind Penetration .................... 39

Figure 21: Wind Power Generated for 30% Wind Penetration ...................................... 39

Figure 22: Wind Speed and Peak LMP using 3 Separate Regions ................................... 40

Figure 23: Average Wind Speed and Peak LMP with Low Wind Speed Data Omitted ... 41

ix

Figure 24: Wind Speed, Modelled Generation and LMP for 01/01/2009 using 9 Locations

........................................................................................................................................ 41

List of Tables

Table 1: Cost-to-Load - Verification ................................................................................ 22

Table 2: Cost-to-Load - BassLink @24MW...................................................................... 24

Table 3: Cost-to-Load - BassLink @289MW ................................................................... 25

Table 4: DG Penetration Requirements .......................................................................... 33

List of Changes

Abstract rewritten to include new objectives and thesis focus

List of tables added

List of abbreviations expanded upon

Thesis objectives rewritten to include the new objectives and focus for the

second half of the thesis subject

Thesis contribution added

Distributed and renewable market framework approach used in the electricity

markets of other countries added to literature review

DG placement in Local Distribution Network added to literature review

Simulation software packages overview added to literature review

Results from first session summarised and put under the heading of previous

outcomes

Methodology and results changed to reflect the work performed in the second

session of thesis.

Future work and conclusion changed to reflect the work achieved over the entire

course of the subject.

x

Abbreviations

AEMC Australian Energy Market Commission

AEMO Australian Energy Market Operator

AER Australian Energy Rules

DG Distributed Generation

DMS Distribution Management System

DNSP Distribution Network Service Provider

LC Load Controller

LMP Locational Marginal Pricing

MC Micro Source Controller

MGCC Micro Grid Central Controller

NEM National Electricity Market

NER National Electricity Rules

NSW New South Wales

NTNDP National Transmission Network Development Plan

OPF Optimal Power Flow

QLD Queensland

RET Renewable Energy Target

SA South Australia

SPP Small Power Provider

TAS Tasmania

VIC Victoria

1

1. Introduction

The NEM consists of an interconnected network between Queensland, New South

Wales, the Australian Capital Territory, Victoria, Tasmania and South Australia.

Traditionally power generation in the NEM has been vertically integrated and centrally

dispatched. However due to the broad distance the electricity network covers it brings

with it very high capital costs for transmission upgrades and investment in new lines [1].

With the cost of residential electricity having risen by 91 per cent over the past 5 years

consumers have started to use power in a conserving manner [2]. As a result the demand

for electricity in the NEM has slightly declined and stabilised. Thus causing the new

generation investment outlook model in [1] predicting the generation investment cost

to be $26 billion. This is significantly lower than the previous prediction of $65 billion in

the 2010 NTNDP report.

Over the past few years there has been an increase in distributed and renewable

penetration in the NEM. The main driving force behind the increase in renewable energy

generation has been due to the mandated RET of 45000 GWh/yr by 2020 and the

introduction of the Australian carbon pricing mechanism which commenced July 1st

2012 [3]. A side effect of the increase in distributed and renewable generation is that

there are high costs associated throughout the levels of generation, transmission and

distribution. These costs are mostly due to the electricity network needing to be

upgraded due to reliability, safety and security reasons. However due the RET and

carbon pricing mechanism it is becoming unviable to build new coal-steam generation

units. Thus making distributed and renewable generation more prominent power

sources in the NEM.

The Australian Electricity Market Operator (AEMO) operates the NEM of which they are

responsible for its reliability and security [4]. As set out in the Australian Electricity Rules

(AER) each generator participating in the NEM power pool requires a connection

agreement with AEMO. The connection agreements generally split generation units into

scheduled, semi scheduled and unscheduled categories depending on their production

capacity and availability. Scheduled and semi scheduled generation are controlled

2

through a spot price market controlled by AEMO. The spot markets design means that

it is ideally based on the traditional centrally dispatched electricity market framework.

Distributed and renewable generation are located throughout the different levels of

power transmission and distribution. In order for renewable generators to participate in

the spot pricing market directly, complex prediction algorithms are required so that they

can make their day ahead bid offers to AEMO. Distributed and renewable generation

can also be used to reduce the cost of the local spot price. This can be made possible by

the distributed generating unit being operated in an islanded mode in its local

distributed network.

If the correct framework is in place it can be made possible for AEMO to schedule

distributed generating units at times of high electricity spot prices. Thus reducing the

average maximum cost of electricity at particular nodes. Distributed and renewable

generation can also affect the spot prices of adjacent nodes through times of high power

export.

1.1 Thesis Objectives

This thesis will cover the following objectives to help determine the effects distributed

and renewable generation have from an electricity market operational cost perspective.

Research how the electricity market framework can be modified to better

include distributed and renewable generation,

Test the effects DG has on the operational costs of a power system by performing

static OPF with Matpower and dynamic power flow with PLEXOS,

Create a function that allows static OPF in Matpower to be ran at 48 intervals to

simulate dynamic power flow over 24 hours,

Determine possible incentives that will help increase the level of renewable

generation penetration in the electricity market.

3

1.2 Thesis Organisation

This thesis consists of six chapters with chapters 1 and 5 being the introduction and

conclusion respectively. In chapter 2 an overview of the existing NEM framework is given

to better understand how distributed and renewable generation are currently taken into

consideration. This chapter also covers the advantages and disadvantages of introducing

distributed and renewable generation into the electricity market from a system

operator’s perspective. It then explores current modelling methods that have been used

to study the effects of distributed and renewable generation in power networks. It

covers the existing factors that are contributing to the push for renewable power

generation into the NEM. Finally it introduces a proposed method of DG placement that

Local Distribution Companies (LDC’s) could use to make DG more attractive to investors

by offering benefits to both the LDC and investors.

Chapter 3 outlines the results of testing performed in the first half of the sessions. It

shows the effects of introducing DG at a single bus then moves on to model DG at

multiple busses. This analysis was used to study the advantages of DG in a power system

in regards to the spot price.

Chapter 4 then moves on to the most recent methodology and results obtained in the

final session of the thesis subject. This is split into two distinct sections with the first

being an overall NEM model and the second being a regional model using the IEEE 118-

Bus test system. The first section being the NEM model uses the two programs Plexos

and Matlab (matpower toolbox) to study the possible advantages of DG can play in

interconnector contingency situation (reduced line flow capacity). The second section

first reconfirms the placement strategy of DG then moves on to modelling the effects of

solar and wind generation could have at levels of 10, 20 and 30 per cent penetration

into a region the size roughly equal to that of NSW or QLD.

Finally in chapter 6 an outline of future work to be performed is given. This chapter will

provide an overview of how the current research can be furthered in order to include

the cost of operating renewable DG in a power system. Also it will touch on expanding

the model to include a wider selection of data and run over a longer time period.

4

1.3 Thesis Contribution

Contribution Page

Provides a guideline as to which Local Distribution Companies could make it

easier for small power suppliers to install DG while benefiting both parties 15

Illustrates how the implementation of DG can reduce the spot price 18-19

Provides a regional 5-node model of the NEM in the two different software

packages Matlab and PLEXOS. These are used to illustrate the effects DG can

have on the overall power system during interconnector contingency situations.

It also shows the differences between the capabilities of the different software

packages.

21-30

Reconfirms the optimal placement of DG with the goal of reducing the spot price

throughout the system. 31-33

Provides a model which can be used to perform the static optimal power flow at

30 minute intervals while implementing DG in the form of solar or wind

generation. The results of these simulations are used to analyse the effects that

different penetration levels of renewable generation have on the pricing of the

electricity network.

34-43

Provides a comparison of how wind and solar generation can be beneficial to the

NEM. It also discusses how these technologies could work in conjunction with

each other and the benefits large scale power storage would provide to these

technologies in the future.

43-44

5

2 Literature Review

2.1 Australian National Electricity Market

The electricity network on the east coast of Australia was traditionally a vertically

integrated system of state government owned and operated assets. In 1998 the NEM

began operation of the longest interconnected power system in the world [5]. The

introduction of the NEM has allowed for privately owned generation and transmission

assets to operate on the east coast of Australia. The electricity market is operated by

the AEMO [4]. A wholesale spot market is used by AEMO to operate the power pool

through which generator and retailers trade electricity. It is the responsibility for the

National Electricity Rules (NER) governing the NEM to be reviewed, amended and

expanded by the Australian Energy Market Commission (AEMC) [4]. It is the AER

responsibility to enforce the NER.

The growth in energy consumption has gradually declined over the past five decades

leaving Australia as the 18th largest energy consumer in the world [5]. With an abundant

supply of natural resources Australia’s generation mix consists of approximately 90%

non-renewables and 10% renewables [3]. Non-renewable power sources are mostly

coal, oil and gas. The renewable generation mix over the past decade has change

significantly. In [3] it can be seen that the hydro component of installed capacity of

renewable energy has decreased by 30% due to a significant increase in wind and solar

installation.

2.2 Wholesale Electricity Market

The traditional electricity market framework by which AEMO operates is based upon a

spot market through which electricity is bought and sold through a power pool [4].

AEMO follows the process of first determining the required demand level and gathers

generation offer prices. Once AEMO has determined the load it then schedules and

dispatches the generator using the generator offers by placing them in a bid stack [4].

They then calculate the spot price by measuring electricity use and generator usage. The

6

final step in the process is for AEMO to settle the market financially by paying each of

the generators for the electricity they have produced.

It is common though for most renewable and distributed generation units to not

participate directly in the spot market. This can be either due to their low power output

or intermittent power output which is reflected in their connection agreement [4].

However all power generation either large or small can have an effect on local and/or

neighbouring power requirements thus affecting the spot price. It could be in AEMO’s

interest to modify the electricity framework in such a way that they have control over

all distributed and renewable generation.

2.3 Hourly Spot Price

In [6] the hourly spot price, ρk(t), for the kth customer at hour t is defined as (1).

𝜌𝑘(𝑡) = 𝛾𝐹(𝑡) + 𝛾𝑀(𝑡) + 𝛾𝑄𝑆(𝑡) + 𝛾𝑅(𝑡) + 𝜂𝐿,𝑘(𝑡) + 𝜂𝑄𝑆,𝑘(𝑡) + 𝜂𝑅,𝑘(𝑡) (1)

𝛾𝐹(𝑡): 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑀𝑎𝑟𝑔𝑖𝑛𝑎𝑙 𝐹𝑢𝑒𝑙

𝛾𝑀(𝑡): 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑀𝑎𝑟𝑔𝑖𝑛𝑎𝑙 𝑀𝑎𝑖𝑛𝑡𝑒𝑛𝑎𝑛𝑐𝑒

𝛾𝑄𝑆(𝑡): 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑄𝑢𝑎𝑙𝑖𝑡𝑦 𝑜𝑓 𝑆𝑢𝑝𝑝𝑙𝑦

𝛾𝑅(𝑡): 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑅𝑒𝑣𝑒𝑛𝑢𝑒 𝑅𝑒𝑐𝑜𝑛𝑐𝑖𝑙𝑖𝑎𝑡𝑖𝑜𝑛

𝜂𝐿,𝑘(𝑡): 𝑁𝑒𝑡𝑤𝑜𝑟𝑘 𝑀𝑎𝑟𝑔𝑖𝑛𝑎𝑙 𝐿𝑜𝑠𝑠𝑒𝑠

𝜂𝑄𝑆,𝑘(𝑡): 𝑁𝑒𝑡𝑤𝑜𝑟𝑘 𝑄𝑢𝑎𝑙𝑖𝑡𝑦 𝑜𝑓 𝑆𝑢𝑝𝑝𝑙𝑦

𝜂𝑅,𝑘(𝑡): 𝑁𝑒𝑡𝑤𝑜𝑟𝑘 𝑅𝑒𝑣𝑒𝑛𝑢𝑒 𝑅𝑒𝑐𝑜𝑛𝑐𝑖𝑙𝑖𝑎𝑡𝑖𝑜𝑛

It is possible for this equations components to then be split into three main sub

equations representing System Lambda (2), Marginal Value of Generation (3) and

Marginal Value of Network Operation (4) [6].

𝜆(𝑡) = 𝛾𝐹(𝑡) + 𝛾𝑀(𝑡) [𝑆𝑦𝑠𝑡𝑒𝑚 𝐿𝑎𝑚𝑏𝑑𝑎] (2)

𝛾(𝑡) = 𝜆(𝑡) + 𝛾𝑄𝑆(𝑡) [𝑀𝑎𝑟𝑔𝑖𝑛𝑎𝑙 𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑜𝑛] (3)

7

𝜂𝑘(𝑡) = 𝜂𝐿,𝑘(𝑡) + 𝜂𝑄𝑆,𝑘(𝑡) [𝑀𝑎𝑟𝑔𝑖𝑛𝑎𝑙 𝑉𝑎𝑙𝑢𝑒 𝑜𝑓 𝑁𝑒𝑡𝑤𝑜𝑟𝑘 𝑂𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛] (4)

System lambda consists of the “marginal fuel cost” and “marginal maintenance cost” [6].

This is the main component that is used when modelling generation since it takes into

account the cost of producing the required power output in units of $/MWh. When an

Optimal Power Flow (OPF) script is run in Matlab on a power test system it returns a

value of system lambda for each bus. It is this price that is used as the spot price for

modelling purposes.

The marginal value of generation and marginal value of network operations are

relatively small while the power system is operating below capacity. However in times

of high demand when a power system is approaching capacity these values can

significantly dominate the hourly spot price [6]. Hence it is necessary not to neglect

these when performing system analysis.

2.4 Market Modelling and Generator Cost Functions

There are a number of different methods that can be used to model the various aspects

of pricing in relation to distributed and renewable generation in electricity networks.

These include the use of the OPF feature of the Matlab toolbox Matpower [7] and also

market simulation software such as PLEXOS [8]. As a general rule distributed and

renewable generation is normally modelled as a negative load for ease of simulation [7].

This is mainly due to the intermittent nature of renewable power sources.

With the increase in demand and implementation of renewable technologies such as

wind and solar, advanced prediction algorithms have been developed [9]. The exact

details of these algorithms are beyond the scope of this report. However the idea itself

is predominantly essential due to the assumption that these prediction algorithms can

be used to determine a renewable generators day-ahead and 5-minute power output

[10, 11]. This information can then be used by the market operator in the process of

dispatching generators since;

𝑃𝐷𝑒𝑚𝑎𝑛𝑑 − (𝑃𝑤𝑖𝑛𝑑 + 𝑃𝑠𝑜𝑙𝑎𝑟) = 𝑃𝑛𝑜𝑛−𝑖𝑛𝑡𝑒𝑟𝑚𝑖𝑡𝑡𝑒𝑛𝑡 (5)

8

In [6], two dispatch models are used to examine the differences between “wind power

as a constraint in dispatch” and “wind power as a strategic bidder”. The first scenario

models wind as a negative load. This corresponds to the current process of dispatching

wind power as all renewable generation must be bought first. The second scenario

models wind generation as strategic bidders, thus allowing them to become key players

in the power pool. By allowing renewable generators to operate as strategic bidders a

large incentive is introduced since there is a large increase in the profitability of the

renewable generator [6]. The results of [6] indicate a profitability for future renewable

generators however this is at the cost of the consumer. In conclusion to this it can be

seen that the electricity market framework should be modified to benefit both the

generator companies and also the consumers.

In order to model generation sources appropriate generator cost functions are required.

These cost functions are used to determine the costs of running the generators based

upon required power output with respect to fuel and maintenance costs. Hence the

generator cost functions return a value equivalent to system lambda, λ-$/MWh.

Conventional generation sources such as coal and gas are used as the main sources of

dispatchable generation. With black and brown coal used for base load power and gas

used for intermediate power generation. Equation (6) is a second order polynomial that

is used to represent the cost function of coal and gas power generation [10]. The costs

generated through equation (6) are used in the determination of the spot price.

𝐹𝑖(𝑃𝑖) = 𝛼𝑖 + 𝛽𝑖𝑃𝑖 + 𝛾𝑖𝑃𝑖2 (6)

𝛾, 𝛽 𝑎𝑛𝑑 𝛼: 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡𝑠 𝑖𝑛 $/ℎ𝑟

𝑃𝑖: 𝑃𝑜𝑤𝑒𝑟 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑 𝑖𝑛 𝑀𝑊

9

In times of high power demand hydroelectric power generation is often used as a

peaking power source. The main reason behind this is that they have a very fast start up

response time. When producing power hydroelectric generation has no fuel costs.

However peaking plants with a pumping cycle are modelled with the fuel costs that are

required to pump the water back up into the reservoir at a time when the spot price is

lower [6].

In order to model wind as a negative load its power output must be predicted so that

the market operator can schedule the remaining power necessary to maintain network

balance [10]. Prediction of the day ahead wind power generation can be achieved by

using equation (7).

𝑃𝑔𝑒𝑛 =1

2 𝜌 𝐴 𝑢3 (7)

𝑃𝑔𝑒𝑛: 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐 𝑃𝑜𝑤𝑒𝑟 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑 𝑓𝑟𝑜𝑚 𝑤𝑖𝑛𝑑 𝑖𝑛 𝑀𝑊

𝜌: 𝑎𝑖𝑟 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 𝑖𝑛 𝑘𝑔/𝑚3

𝐴: 𝑊𝑖𝑛𝑑 𝑆𝑤𝑒𝑝𝑡 𝐴𝑟𝑒𝑎 𝑖𝑛 𝑚2

𝑢: 𝑣𝑒𝑙𝑜𝑐𝑖𝑡𝑦 𝑜𝑓 𝑤𝑖𝑛𝑑 𝑠𝑝𝑒𝑒𝑑 𝑖𝑛 𝑚𝑠−1

As wind power is one of the most mature forms of renewable energy generation

technologies its future reduction in cost is predicted to not be as significant as other

technologies [24]. This in turn allows for future implementation costs to be calculated

relatively simply.

10

As solar is also modelled as negative load it is necessary to calculate its day ahead power

generation using equation (9) as found in [10].

𝐸𝑡 = 3.24 𝑀𝑝𝑣(1 − 0.0041(𝑇𝑡 − 8))𝑆𝑡 (8)

𝐸𝑡: 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐 𝑃𝑜𝑤𝑒𝑟 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑 𝑓𝑟𝑜𝑚 𝑠𝑜𝑙𝑎𝑟 𝑖𝑛 𝑀𝑊

𝑀𝑝𝑣: 𝑀𝑎𝑥 𝑝𝑜𝑤𝑒𝑟 𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑒𝑑 𝑖𝑛 𝑀𝑊

𝑇𝑡: 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 𝑖𝑛 °𝐶

𝑆𝑡: 𝑆𝑜𝑙𝑎𝑟 𝑖𝑟𝑟𝑎𝑑𝑖𝑎𝑛𝑐𝑒 𝑣𝑎𝑙𝑢𝑒 𝑖𝑛 𝑘𝑊ℎ/𝑚2

2.5 Introduction of Renewable and Distributed Generation in the NEM

DG may consist of a variety of generation types including renewables and non-

renewables. In the Australian NEM context power generation of less than 30MW is often

considered to be DG [12]. Sections 2.2.3 and 2.2.5 of the NER define the two main

categories of existing DG being Non-Scheduled and Non-Market generators respectively

[13]. However with the increasing rate of large scale renewable penetration into the

NEM it is becoming harder to define DG as the different generators can fall under the

majority of generator categories. The main factors that are effecting it include the

distributed power output, the availability of supply, infrastructure costs and also

connection fees as discussed in [12].

2.6 Renewable Energy Target and Emissions Trading Scheme

With the RET set at 45,000 GWh/yr for the year 2020 and the introduction of the

Australian carbon pricing mechanism [3] there has been a major increase in the

commissioning of renewable power generating units in the NEM. Since the NEM is

traditionally a vertically integrated centrally dispatched system it presents a set of

challenges that have to be overcome in order to integrate distributed and renewable

generation into the electricity network. This can be achieved by modifying the existing

market framework by which AEMO operates. In [24], it is predicted that once the carbon

price becomes greater than $50 per tonne the lower cost option of rebuilding the NEM

11

will be with renewable energy. Since NEM coal and gas generators would be

“considerably more” expensive, if faced with international prices [24], it is highly likely

that these cheaper conventional forms of generation will become significantly more

expensive if the resources could be easily sold on the international market. With the

introduction of liquefied natural gas terminals on the east coast of Australia this has a

high probability of occurring. Thus in turn will act as an incentive for electricity

generation companies to invest their money elsewhere in other technologies such as

renewable generators.

2.7 Effects of Renewable and Distributed Generation on Electricity

Market

Renewable and distributed generation can have many effects on the electricity network

of which must be taken into consideration into the electricity market framework.

Market regulations and framework play a significant role in the process of

commissioning new generators. One current deterrent in the NEM is the fact that all

generators no matter their size have to pay the same connection fee if they intend to

participate in the power pool [14]. There is also a great deal of manual processing of

each application due to AEMO not having a standardised approach to small generator

registration [14]. If the correct framework were to be put in place then small generators

could operate more freely in the power pool.

Although a framework that were to include small distributed and renewable generation

would increase the costs due to the complexity of monitoring and running the network.

It would however provide a much more efficient and secure electricity network. The use

of DG and renewable generation as the ability to reduce the future cost of localised

electricity as the technology further develops and becomes cheaper to install and

operate. As the level of renewable generation penetration increases throughout the

network, the required level of conventional generation will be offset. This will not only

extended the lifespan of the finite fuel source, but also significantly reduce the amount

12

of emissions produced by the electricity network. Finally by introducing a set of market

regulations that allows distributed and renewable generation to be introduced and

operated in the electricity network it will open many new doors for different jobs.

Electricity market frameworks vary between the different power systems throughout

the world with the main difference being how the power pool operates. The Nord pool

in Scandinavia is optional as opposed to mandatory in Australia, England and Wales [12].

However in California it is only mandatory for the three main private utilities, ensuring

no one utility can achieve full market power. An advantage of the Californian market

framework are the incentives present in the fee structure. Unlike the Nord Pool,

Austraian NEM and the England and Wales annual fixed fees, there is only a one time

application fee in California [12].

The introduction of a new law in California will require utilities to get 33 per cent of their

electricity from renewable sources. The proposed decision [26] will require up installed

energy storage to reach 1.3 GW by the year 2020. This will be spread throughout the

three main utility companies; Southern California Edison (580 MW), Pacific Gas and

Electric (580 MW), and San Diego Gas and Electric (165 MW). As a result of this it is very

likely that energy storage technology will become significantly more affordable during

this time.

Germany is another country that is leading the way in terms of renewable and

distributed energy penetration. The German government has revised aconcept of 50 per

cent renewable in the gross electricity consumption until 2030 as well as well as a

complete renunciation of nuclear power until 2022 [27]. By the end of 2011 the

renewable energy generation in Germany had already exceeded 20% gross electricity

generation [27]. The grid-regulating Federal Network Agency has stated that energy

storage in the possession of grid operators is not allowed to operate in the markets while

not being used for grid support. Another future framework proposal is that a storage

13

operator could be introduced into the market which will work in conjunction with the

market operator.

The implementation of distributed and renewable generation has the ability to reduce

the LMP [15]. As the LMP is the price to provide the required energy for each bus for the

next unit of demand it directly effects the overall spot price. Thus by distributed and

renewable generation being present throughout the network it has the ability to

maintain an overall lower spot price. It is capable of achieving this by having busses use

the cheaper locally generated electricity first before they import from centrally

dispatched generation.

In [15], a 5-bus test system was designed and implemented in Matlab. This model

consisted of three centrally dispatched generators and one large wind generator

representing a 14.6% renewable penetration level. The results of this paper indicate that

a large amount of money is able to be saved as the wind generator is capable of

supplying a large portion of load previously supplied by an expensive centrally

dispatched generator.

Distributed and renewable generation are already affecting transmission investment

requirements. Schemes such as RET and government incentives have significantly

increased rooftop solar generation and hot water throughout the NEM. It is believed

that the recent solar increase has accounted for 53 per cent of the reduction in energy

demand since 2008 [2].

The NEM has interconnector transmission links which allow the trade of electricity

between its 5 regions [2]. This allows each region to draw electricity from neighbouring

regions during times of high electricity demand [2]. As the introduction of DG increases

the amount of power generation capacity will increase throughout each region thus

minimising the need for the capacity of interconnectors to be increased. In [1] it is

estimated that the new investment in transmission is $4 billion over twenty years which

14

is considerably lower than the previous estimate of $7 billion over twenty years.

However there will still need to be transmission investment to cover the costs of

upgrading aging assets, addressing local transmission issues due to new generation and

addressing medium and low voltage transmission and distribution needs [1].

A study in [25] suggests that the most beneficial option for future interconnector

investment is a northern ac option that joins Wilmington in SA to Mount Piper in NSW.

This interconnector option however would run through Broken hill, thus enabling large

amounts of wind generation to be connected at both SA and NSW and fed throughout

the system.

Microgrids are low or medium voltage distribution networks consisting of distributed

and renewable generation, possible storage devices and controllable loads with the

ability to be operated in grid-connected or islanded mode [16]. Microgrids have the

ability to improve the reliability and security of the electrical network if included in an

economic dispatch system due to the large diversity of generators [10]. Currently there

is much research underway to determine the most cost effective ways to operate

microgrids. The economic dispatch in each microgrid can vary significantly depending on

the generator types and load profiles.

In order the operate a microgrid efficiently a balance between local generation costs,

energy purchased from the main grid and energy sold back to the main grid needs to be

established [16]. It is suggested in [16] that the hierarchical control architecture be split

into three categories; Distribution Management System (DMS), Microgrid Central

Controller (MGCC), and Micro Source Controller and Load Controller (MC and LC).

Integration into to the NEM would require the central controller AEMO to have control

over the DMS while having the MGCC operate the microgrid locally. Local control over

the microgrid enables a high degree of local optimisation [16], and also reduces the need

for AEMO to perform a large amount of complex optimisation calculations.

15

In [10] an economic dispatch model was implemented to study the effects of wind and

solar implementation in an islanded microgrid with conventional and combined heat

cycle generation. The results of the simulation indicate that by including renewable

energy credits and wind energy into the microgrid the total cost of generation can be

reduced.

In [20] a model is used which optimises the Local Distribution Company’s (LDC) choice

of DG placement and development approval based upon submissions from Small Power

Producers (SPP). As each LDC has the most accurate knowledge of the requirements of

their local distribution network they can easily assess the benefits and disadvantages of

DG connection points. To help benefit each of the LDC’s and SPP’s each LDC could create

a DG priority connection list which they share with SPP’s. The process could be based

upon the following procedure;

1. Each LDC creates a DG priority connection list based upon their individual

network requirements.

2. Individual SPP’s submit their own design proposals based upon the requirements

set out in the LDC connection requirements.

3. The LDC chooses the proposal (if any) which best suits their requirements.

4. AEMO assess and process the application ensuring that it meets their standards.

5. Once approved the SPP builds/commissions/operates the new DG in the

distribution network.

16

2.8 Simulation Software Packages

There have been many market modelling simulations run on MATPOWER to study the

effects of distributed and renewable generation in electricity networks. In [15] a wind

farm is implemented between a generation and load bus. The system was designed to

reduce the need to schedule the second conventional generator with a much higher

operational cost. The results of the simulation show that as the generation from the

wind farm increases, the generation needed from the secondary conventional generator

decreases. Thus resulting in a decrease in the cost to serve the load. In [17] it is also

shown that the gain that wind farms can make while operating as strategic bidders

outweighs the cost of not being able to produce power in times of low wind.

Another report includes [28] which presented a methodical approaching into how

conventional DG could be placed into a power system by using the IEEE 118-Bus test

system. The results of this report indicated that it was possible to reduce the LMP in a

Standard Market Design (SMD) with the introduction of DG. This also led to larger more

expensive conventional generators to become redundant.

Unlike in the Matpower OPF test systems it has not been found in PLEXOS as to how

reactive power can be modelled. This bought with it the challenge of building the 14-

Bus test system in PLEXOS and getting reliable results. The generation coefficients had

to me modified in PLEXOS in order to achieve a cost-to-load price that corresponded to

that observed in Matpower. However as reactive power is not traded through the spot

market it was not taken to be a significant concern.

As Matpower performs the OPF simulations it can be used to model the stability of the

system at different loads and generation at set values. Since PLEXOS operates as a

dynamic market model it has the ability to perform economic dispatch on a varying load

profile. By using these two programs in conjunction with each other it is possible to

determine the stable load levels and LMP using Matpower, then perform economic

17

dispatch to determine the generation costs and cost-to-load using PLEXOS. The use of

the economic dispatch in PLEXOS can also be used to determine the amount of DG

required in a system to avoid the scheduling of the highly priced generator.

PLEXOS is used throughout multiple countries for the use of power market modelling

[8]. It has the ability to include or exclude a significant number of different variables that

are not easily modelled in other software packages. Companies such as AEMO use the

software to simulate the current power market but also future development plans.

Using the knowledge learnt of matlab and Plexos over the duration of the thesis a NEM

model was created in each program. This model was used to study the effects that

interconnector ratings could have one the NEM.

18

3 Previous Outcomes

The expectation of the preliminary thesis in respect to testing and modelling was to

reduce the average and peak LMP’s which would lead to a reduction in the spot price.

As DG was implemented it was found that the LMP would reduce at the point of

connection and neighbouring busses. The introduction of DG across multiple busses

resulted in a more significant reduction in the spot price as compared to implementing

DG on a single bus.

3.1 Implementation of DG at Different Busses

A systematic approach was taken throughout the testing and simulation process. Initially

the 14-Bus test system was studied in order to develop an in depth understanding of its

design and components. Once the base results had produced incremental sizes of DG

were then implemented. This was done so as suggested in [7] and modelled as a

negative load. This then led to the design, testing and analysis of a 10-30% DG

penetration level placed at strategic bus locations.

Before any DG was implemented a reference line was graphed at the location of the

second highest LMP. This process was used to clearly visually indicate which bus had the

highest LMP. By modelling DG as a renewable source it could be modelled as a negative

load and had no generation costs associated with it.

At a level of 30% DG the LMP across the system became relatively equal for each of the

load busses. Figure.1 shows the comparison between the LMP of each bus at the

different levels of DG implementation.

19

Figure 1: DG Effect on Cost in 14-Bus System

In Figure.1 it can be seen that as the percentage of DG increases the average LMP is

reduced. Initially the introduction of DG has very little effect on the LMP however as it

approaches the RET of 20% renewable energy penetration it drops by almost $2 /MVA-

hr. As the penetration level increases further to 30% the LMP drops by approximately

$4.5 /MVA-hr.

20

4 Methodology and Results

4.1 NEM Model

The load data used for all simulations was obtained from AEMO for the period of January

1st 2009. This date was chosen as it was used in previous models in other studies, as well

as the necessary wind and solar data only being available for this date. The daily load

data can be seen below in Fig. 2 and also Appendix C.1.

Figure 2: 1st January 2009, NEM Load Data [18]

The generator data used in the creation of the NEM model was based upon the same

information used in [18] which was obtained from [19]. This generator data used for

each state can be seen in Appendix C.2.

The interconnector data however was obtained from [23] and is shown Fig. 3 and Fig. 4.

21

Using the original Generator and Interconnector data the following PLEXOS model was

created and its overall layout drawn in Microsoft Visio for easier interpretation as shown

below in Fig. 3;

Figure 3: PLEXOS NEM Model Layout

The above model was created in PXOS using all referenced in this section. This process

took a considerable amount of time as an understanding of the program had to be

developed. There were also a few issues when verifying it against some of the results

obtained in [18]. An exact replica could not be produced however it was very close. After

extensive testing and analysis it was believed that the differences were most likely due

to inconsistencies between the two data sources [18] and [19] as well as other variables

and constraints that may have also been implemented. Another difference was that the

model in [18] used different interconnector values. However testing confirmed that it

did not affect the system relative to the differences observed. Overall though the model

created produced results that were very similar to that in nature of real life NEM power

flow and appropriate pricing.

22

The NEM model created in Matlab was designed based upon the previously created

PLEXOS model. However due to the differences in the programs the two models could

not be created identically. To compensate for this the Matlab model was designed to be

as similar as possible to the PLEXOS model and converge with a cost-to-load value within

0.1% difference. As the Matpower OPF in Matlab is based upon static simulations only

the data for the first period of January 1st 2009 was used for this section. The reason for

using this one load data set alone was due to simplifying the model as well as

observations to be made not requiring changing load data.

The numerical values for the generator cost functions were taken from [18] and scaled

until the program converged within the set 0.1% tolerance. It was found that the scaling

factors for the generator polynomial coefficients were 0.1 and 1.265 respectively. These

can be seen under section “Generator Cost Data” in Appendix C.3.

Table 1: Cost-to-Load - Verification

MATLAB PLEXOS

Cost-to-Load ($/period) 208082.55 208341.37

As Matpower models the interconnectors differently to PLEXOS the interconnector

values were slightly different and can be seen in Fig. 4. The main difference being that

the rating of power flow was equal in both directions.

23

Figure 4: MATLAB NEM Model Layout

The NEM model created in matlab as shown in Appendix C.3 was designed to perform

just one OPF for the first period of load data for 1st January 2009 as it made it easier to

verify against the Plexos model. The other main reason for not designing this system to

operate for the 48 half hour periods of the day was due to it being designed to simulate

interconnector capacity restrictions. Thus the only variable that would be required to be

changed would be the interconnector’s ratings.

Another difference that was observed was that the LMP for each state was different.

However the final objective function value was very similar between the models. If the

model was required for other types of analysis it would be best to modify it so that it

was as similar to the Plexos model as possible.

24

One of the main causes of high electricity spot prices is the inability to serve the required

load. This can occur by a number of different ways, with the loss of an interconnector

being one of the most severe during times of peak load. In order to observe the effects

of this the PLEXOS and Matlab models created were modified to simulate a contingency

situation on the VIC-TAS (BASSLINK) interconnector.

The interconnector between TAS and VIC was set to 5% capacity (24MW). The MATLAB

and PLEXOS models were then re-run. It was expected that the price to serve the load

should have increased noticeably due to the inability to export cheaper power between

TAS and VIC. However the Matlab model failed to converged as the load at TAS was

unable to be served thus invalidating the OFV. In PLEXOS a valid result was produced as

the cost to serve the load increased to 3255430.08$/period and TAS was required to

dump load as a result of insufficient power supply.

Table 2: Cost-to-Load - BassLink @24MW

VIC->TAS = 5%(24MW) MATLAB PLEXOS

Cost-to-Load ($/period) N/A 3255430.08

Since the MATLAB function did not converge with an interconnector value of 24MW

between TAS and VIC, the value was set so that TAS would be required to operate its

generators at 100% and MATLAB should also achieve convergence. This value was found

to be 289MW (IC rating = T LOAD – T Gen) and was entered into PLEXOS in order to compare

the results. As the MATLAB model converged it was expected that there should not be

a significant price difference in the cost-to-load, however it should increase due to TAS

needing to operate at 100% generation. The following results in Table 3 were found;

25

Table 3: Cost-to-Load - BassLink @289MW

VIC->TAS = (289MW) MATLAB PLEXOS

Cost-to-Load ($/period) 208631.85 4591480.97

Initial analysis of this result leads the observer to believe that this result is incorrect.

However the huge difference in price is due to the differences in the two programs used

and also the models created for each as they are not identical. In the MATLAB model the

OFV remains low as the system is still able to support the TAS load (requirement of

convergence). On the other hand the PLEXOS program has the capability to dump load

when required. This is a result of generation not being able to meet the required load

with leads to the repercussion of reaching the ceiling price ($10000 as PLEXOS default).

Thus leading to the dramatic increase in the overall cost-to-load. Fig. 5 below represents

the financial consequence of generation not meeting demand:

Figure 5: Bass Link Contingency Situation 1

The implementation of DG has the ability to benefit this situation as the increase in local

distributed generation can result in the reduction in number of interconnector

26

contingency situations. Figure 6 is a representation of the effect 21MW of DG would

have for the situation shown in Fig. 5:

Figure 6: Bass Link Contingency Situation 2

For the first period alone the addition of 21 MW DG would create a saving of (4591480-

193757) 4397723 $/period. In reality the occurrence of reaching the ceiling price is

relatively uncommon and does not stay at a sustained value as shown above. The reason

for the model being set this way is to show two things; the financial penalty faced in

contingent situations, and the ability DG can have in reducing relatively volatile prices

during contingent situations.

These results indicate two main outcomes; the first being that MATPOWER is not

capable of providing valid results once it is unable to achieve convergence in its power

flow whereas PLEXOS has the ability to dump load, and the second being that DG has

the ability to minimise the occurrence of the ceiling price being met as it provides

additional generation to the system.

27

The PLEXOS NEM model was then modified to generate 24hr results for different

limitations on the Victoria to Tasmania, Bass Link connection. The 24hr total cost-to-load

for ratings of 100%, 98%, 96%, 94% and 50% (480, 470, 460, 450 and 240 MW

respectively) were implemented and are shown below in Fig. 7

Figure 7: NEM Contingency Analysis

The results above are not identical to that of which occur in reality due to Tasmania

having a significant amount of hydro generation which has not been modelled due to

time constraints. However the results are representative of the significant effect IC

contingency situations can have on a region if the region heavily reliant on the IC to

support their daily load. Once the rating of the Bass Link IC falls to a value that results in

the supplied electricity generation being less than the load, the volatility of the cost to

the system will be very similar to that shown in Fig. 7.

960695620169081

2528305535520736

208395284

±480 ±470 ±460 ±450 ±240

Co

st (

$)

Interconnector Rating (MW)[480MW = 100%], [240MW = 50%]

24hr Cost-to-LoadBassLink Contingency Analysis

NSW QLD SA TAS VIC Total Cost

28

Below in Fig. 8 the periodical spot prices are shown for normal operating conditions for

conventional generation in the NEM for 1st January 2009 with the Bass Link IC operating

at 100% rated capacity (±480 MW).

Figure 8: Spot Price - Bass Link @480MW

The average spot price for Tasmania is significantly greater in Fig. 8 above as it is required

to import a large proportion of its generation requirements due to the total local

generation being relatively small in comparison to its load.

Figure 9: Spot Price - Bass Link @470MW

In Fig. 9 the reduction of the IC rating down to 470MW results in the spot price hitting

the models ceiling price of 10000 $/MWh between periods 41 and 42. This results in the

0

10

20

30

40

50

60

70

80

90

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47

$/M

Wh

1/2 hr Period

Price (BASSLINK Rating @ ± 480MW)

NSW QLD SA TAS VIC

0

2000

4000

6000

8000

10000

12000

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47

$/M

Wh

1/2 hr Period

Price (BASSLINK rating@ ± 470MW)

NSW QLD SA TAS VIC

29

jump in the daily cost-to-load as seen in column 2 of Fig. 7 from $9606956.15 to

$20168080.75, a cost increase of 210%. Thus from these results DG should be

considered as a solution to reducing the regions dependency on the interconnector. DG

and renewables are not always favourable due to their large initial costs. However since

they have the ability to reduce the likelihood of IC contingency situations occurring the

implementation of DG is a benefit to the entire NEM as it help avoid these expensive

situations. As DG has the ability to reduce the transmission requirements it in turn

offsets the transmission investment costs required, while at the same time actively

supplying the region with electricity. This has the lead on effect of reducing the

requirements of conventional centrally dispatched generation. Thus reducing the

demand on fossil fuels such as coal and gas, resulting in an increased lifespan of these

resources.

30

4.2 MATLAB 118-BUS DG Modelling

The 118-Bus test system was chosen as it best represents that of a network of similar

size to the NSW and QLD regions. Thus allowing for an analysis on the effect that large

scale DG and renewable penetration could have at a regional level throughout a large

power system such as the NEM. The topographical layout of the 118-Bus model is shown

in Fig. 10. However due to the poor clarity further reference can be taken from the IEEE

118-Bus model freely available in the Matpower toolbox for Matlab.

Figure 10: 118-Bus Test System Topography

31

The 118-Bus model “case118.m” was ran using the matpower toolbox in Matlab. The

results were then used to plot the load and LMP of each bus as shown in Fig. 11.

Figure 11: Load and LMP of Each Bus in the 118-Bus Model

Before starting the renewable DG modelling a test was first performed to confirm that

it was more economically viable to implement DG on the most expensive LMP buses

rather than the most loaded busses. To achieve this 0-10MW of DG was modelled using

the negative load approach on first the most expensive LMP busses (41 and 44

respectively) and also the most loaded bus (59) in separate simulations. Using the LMP

out of each set of results an array was created and used to plot the effects the DG steps

had on each of the test cases. This was used for a visual comparison and can be seen in

Fig. 12 and Fig. 13.

32

Figure 12: DG Effect on LMP when Placed on Most Expensive Busses

Figure 13: DG Effect on LMP when Placed on Most Loaded Busses

From the results shown in Fig. 12 and Fig. 13 it can be seen that it is more effective to

implement DG on the more expensive LMP busses rather than the most loaded busses.

Thus concluding the assumption was correct due to Fig. 12 being showing the LMP being

more sensitive to change when DG placement is based upon existing LMP prices. In

conclusion to this finding all DG implemented in further simulations will be based upon

the LMP prices in descending order.

33

As stated in [15], most power systems will be able to reliably handle a penetration level

of up to 30 per cent renewable power generation without significant alterations thus a

value of 30 per cent was chosen. This value is greater than the 20 percent RET total

however it allows for some of the generation lost due to intermittent renewable supply.

Thus it means that there is a much greater chance that 20 per cent of generation will be

supplied by renewable generation more often.

In Excel the 118-Bus LMP data was sorted into descending order. Using the automatic

sum feature the number of busses required to produce 10, 20 and 30 per cent renewable

generation were found. This was achieved by selecting the busses in descending order

of LMP that gave a vale slightly higher than the generation requirements. Selection was

done in this manner to maintain a positive load on each bus at all times. The generation

targets and number of required busses are shown in Table 4. The generation data and

required scaling factors can be seen in Appendix D.1;

Table 4: DG Penetration Requirements

Penetration Level

(%)

Generation Requirements

(MW)

Number of Busses

10 424.2 15

20 848.4 21

30 1272.6 36

34

Initially 48 period solar irradiance data was obtained from [21] for 1st January 2009 at

Wagga Wagga, NSW, Australia. This data was required as it is representative of the ‘St’

variable in equation (9). The solar irradiance data can be seen in Appendix D.2. The

“case118” matpower script was modified into three new functions;

case118_CaseSolar_010, case118_CaseSolar_020 and case118_CaseSolar_030. Each

using the scaling factors calculated in AppendixD.1. The modified ‘mpc.bus’ section of

the matpower function for 30 per cent solar penetration levels can be seen in Appendix

D.3. The new solar case files created were modified to allow for the variable Et, total

solar generation, to be passed into them and used throughout as the variable Psolar.

This variable, Psolar, in conjunction with the scaling factors allowed for the solar

generation to be modelled as a negative load as shown in the extract of code in Fig. 14;

Figure 14: Solar Power Implementation in Matlab

Figure 15 is an extract of code used to compute the results for each of the 48 OPF

simulations ran;

Figure 15: Solar Penetration Data Loop

35

Figure 16: Average and Peak LMP 24hr Prices for 10% Solar Penetration

The results in Fig. 16 indicate that at a value of 10 per cent Solar penetration spread out

upon the first 15 of 180 most expensive busses the peak price is reduced to 40.53 $/hr

down from 41.25 $/hr, a saving of 72 c/h. This could be considered only a small saving,

however in the case of a conventional generator needing to switch off for maintenance

or in the event of a trip during the day it could be of great financial benefit. This is due

to the additional power supplied by the network via the solar PV generators. In order

for the solar to be of a greater financial benefit a more expensive conventional generator

of the same output capacity could be decommissioned or re-categorised to operate as

a peaking plant if required. As the nature of the curve in Fig. 16 is relatively flat

throughout the majority of the day it indicates that the price will remain relatively stable

and predictable at a 10 per cent penetration level. Thereby reducing the complexity

required for the market operator when estimating the spot price.

39.2

39.3

39.4

39.5

39.6

X: 25

Y: 39.18

Ave

rag

e L

MP

($

)10 per cent Solar Penetration

[Mpv1 = 4.242 (424.2MW)]

X: 5

Y: 39.53

0 6 12 18 24 30 36 42 48

40.6

40.8

41

41.2X: 6

Y: 41.25

Period (1/2 hr)

Pe

ak L

MP

($

)

X: 25

Y: 40.53

36


Upon implementation of 20 per cent solar penetration it was expected that the curve

would follow that of Fig. 16 with a reduced floor price and a sharper descent into the

generation and sharper ascent out of the generation time. Thus approaching a square

step function. However after simulation it was found that for 20 per cent penetration as

shown in Fig. 17, the floor price decreased but dipped down to a central point when

solar penetration was at its maximum.

The original prediction for the final simulation of 30 per cent solar was similar to that

predicted for 20 per cent with the peak price representing a unit step function. However

from the previous results found for a 20 per cent penetration level it was predicted that

the curve would either flatten out again as in Fig. 16 or the peak in the minimum floor

price would reduce.

38.5

39

39.5

40

X: 6

Y: 39.53

Ave

rag

e L

MP

($

)

20 per cent Solar Penetration[Mpv2 = 8.484 (848.4MW)]

X: 25

Y: 38.3

0 6 12 18 24 30 36 42 4839.5

40

40.5

41 X: 6

Y: 41.25

Period (1/2 hr)

Pe

ak L

MP

($

)

X: 25

Y: 39.98

37


After performing the final simulation for a penetration level of 30 per cent solar

generation it was found that the shape of the curve for the maximum LMP price started

to resemble that of the average LMP price for the day as shown in Fig. 18. A comparison

of the average spot price for each of the penetration levels as shown in Fig. 16, 17 and

18 shows that they all follow a similar curve shape. However at a 10 percent penetration

level solar only offers a slight benefit and may not be financially viable. Whereas at a

penetration level of 20 per cent and greater the renewable generators start to create

significant reductions in the LMP.

37

38

39

40

X: 25

Y: 36.47

Ave

rag

e L

MP

($

)

30 per cent Solar Penetration[Mpv3 = 12.726 (1272.6MW)]

X: 6

Y: 39.53

0 6 12 18 24 30 36 42 4838

39

40

41

X: 25

Y: 38.9

Period (1/2 hr)

Pe

ak L

MP

($

)

X: 6

Y: 41.25

38

The wind penetration models were created in the same manner as the solar models. The

difference being that they simply used a different power generation equation and wind

data was used in this section instead of solar data. To calculate the wind power

generated equation (7) was used. The wind data was obtained from [22] and can be seen

in Appendix D.4. Figure 19 is an extract of code used in the simulation to generate the

wind penetration results for the same 24 hour period;

Figure 19: Wind Penetration Data Loop

As the electrical power produced by wind turbines is proportional to the wind speed

cubed its power output curve is random in nature. Unlike solar with which it is known

what time the sun rises and falls while being predominantly affected by shading, wind

speed is complex to predict. Even once wind speed and the power in the wind is

predicted it is still very volatile in nature with its intermittency. Thus leading to the

importance of turbine location to ensure the power supply is as constant as possible.

Initially all three penetration levels were performed using the wind data from one

location. This in reality is not ideal as it is not practical to implement these sizes of wind

generators all in one location. Due to the significant changes in power output this

scenario would be dangerous to the electricity network for reliability reasons. However

for the first section of this test it is justified as it allows a quick analysis of the overall

benefits and disadvantages that wind turbines can have.

39

Figure 20: Average and Peak LMP 24hr Prices for 30% Wind Penetration

Figure 20 indicates that wind turbine generation can offer significant cost reduction

advantages as seen by the LMP reaching a minimum of $37.50 in the last period of

simulation. However due to the turbines intermittent behaviour in output power as

shown in Fig. 21 the wind turbines should be spread out over a vast area.

Figure 21: Wind Power Generated for 30% Wind Penetration

The results obtained above are not an accurate representation of the overall benefit

wind generation offers as all generation has been calculated from a single set of wind

speed data (Broken Hill Airport). In order to observe the benefit of this type of DG wind

speed was initially taken from 3 different locations (Broken Hill Airport, Rundle Island

and Learmonth Airport). The Wind speed data can be seen in Fig. 22 as well as the

average;

35

36

37

38

39

40

Ave

rag

e L

MP

($

)Average and Peak LMP for 10, 20 and 30 per cent wind penetration

6 12 18 24 30 36 42 4837

38

39

40

41

Period (1/2 hr)

Pe

ak L

MP

($

)

10% Wind Penetration






6 12 18 24 30 36 42 480

424.4

848.4

Period (1/2 hr)

Win

d P

ow

er

Ge

ne

rate

d (

MW

)

Wind Power Generated[1 wind data set: Broken Hill]




40

Figure 22: Wind Speed and Peak LMP using 3 Separate Regions

From the results in Fig. 22 it can be seen that by distributing the wind penetration over

multiple locations the overall spot price is only slightly reduced, minimum LMP of

$40.18. This is predominantly due to peak generation being constrained to have full

rated wind speed at every turbine site simultaneously. A suggestion to this would be to

design the system based upon an overall average wind speed value rather than a peak

value of low probability which will cause an increase in the capacity factor. The above

model relies upon each region supporting 1/3 of the required renewable wind

generation. This however is significantly affected by the constant low wind speed Ut_3

(Learmonth Airport). Thus the simulations indicates that it would not be wise

implementing wind turbines in areas of low wind speed.

The data for Ut_3 was then removed to confirm whether it would in fact be financially

beneficial to remove the low wind speed data. The results in Fig. 23 confirm that it is

slightly more beneficial to implement wind penetration with the reduced number of

regions which have a higher average wind speed. This is shown by the LMP reaching a

minimum value of $39.30. The small change in LMP for the 3 wind speed model as

opposed to the 2 wind speed model was predominantly due to the heavy weighting on

each of the generators (1/3 each). The wind turbine generators in the 2 wind speed

model have an even greater weighting however due to their higher average wind speed

they have the ability to produce a significantly greater amount of power.

2.5

5

7.5

10

Wind Speed and Peak LMP at 30 per cent penetration[3 sets of wind data]

Win

d S

pe

ed

(m

s-1

)

6 12 18 24 30 36 42 4840

40.5

41

Period (1/2 hr)

Pe

ak L

MP

($

)

Peak LMP

Broken Hill Airport

Rundle Island

Learmonth Airport

Average Wind Speed

41

Figure 23: Average Wind Speed and Peak LMP with Low Wind Speed Data Omitted

Figure 23 illustrates that the average wind speed and peak LMP are still fairly volatile

throughout the 24 hour period modelled. In order to try and overcome this volatile

nature a model closer to real life scenarios was created. This model consisted of nine

separate wind speed data sets which can be seen in Fig. 24 and Appendix D.4.

Figure 24: Wind Speed, Modelled Generation and LMP for 01/01/2009 using 9

Locations

Using the nine regions of wind data as shown in Fig. 24 and Appendix D.4 the wind power

output over the course of the 48 period’s smooths due to the increase in the spread of

2.5

5

7.5

10

Win

d S

pe

ed

(m

s-1

)

Wind Speed and Peak LMP at 30 per cent penetration[2 sets of wind data]

6 12 18 24 30 36 42 4839

39.5

40

40.5

41

Period (1/2 hr)

Pe

ak L

PM

($

)

Peak LMP

Broken Hill Airport

Rundle Island

Avewrage Wind Speed

5

10

15

Wind Speed, Power Generated and Peak LMP at 30 per cent wind penetration[9 sets of wind data: Appendix D.4]

Win

d S

pe

ed

(m

s-1

)

500

1,000

Win

d P

ow

er

Ge

ne

rate

d (

MW

)

6 12 18 24 30 36 42 48

37

38

39

40

41

Period (1/2 hr)

Pe

ak L

MP

($

)

Rundle Island

Murrurundi Gap

Cooma Airport

Oakey

Broken Hill

Mount Gambier

Snowtown

Edithburgh

Parawa

Average

Wind Power Generation

Peak LMP Price

42

the wind data. This is due to the system taking the average of the wind speed from the

nine different regions. As a result of the power generation now being less erratic the

peak LMP throughout the 24 hour period also smooths out. Thus indicating that a wide

spread of wind data on average will have less side effects in the overall electricity

network as the varying power supply is spread throughout the network. This makes it a

lot more viable at the large scale generation level as the complexity is reduced for the

market operator, therefore reducing their operational costs.

Figure 24 also illustrates that wind power generation is very attractive to investors since

it has the ability to generate significant amounts of power once the wind speed becomes

greater than half the turbines rated speed. As the wind turbine generation is further

increased its capacity factor should also further increase. This will in turn represent the

minimum amount of wind generation constantly being supplied to the power system.

43

Both wind and solar generation can be beneficial to the electricity network as they have

the ability to provide clean energy with zero fuel costs. However they are limited in

usability due to the intermittent nature of their natural fuel sources. They also suffer

from their large start-up costs that investors are required to put forward. Despite this

these costs will significantly reduce over time as the renewable generation technology

improves.

Solar generation is relatively simple to predict as it mainly depends upon solar

irradiance, temperature and shading. Whereas wind generation is more complex as it

depends upon the forever changing wind speed which affects the power output

exponentially. Thus leading to the trade-off between predictability and generation

capability. Solar is more predictable but is less efficient opposed to wind generation

being harder to predict but offers a much higher generation efficiency. The higher

efficiency of wind turbines and there significantly cheaper production costs has made

them very attractive to investors seeking to invest in renewable generation technology.

The added complexity of predicting wind power generation has in the past been a huge

hurdle for both investors and energy market operators. However over the last decade

prediction methods and accuracy have significantly increased. Thus resulting in the

capability of wind generators to be classed as semi-scheduled or even scheduled

generators. With the penetration level of renewable generation ever increasing it is

becoming more and more important for the market framework around distributed and

renewable generation to be clearly set out.

As wind farms are built throughout eh electricity network they will naturally become

fundamental generators constantly supporting a portion of the networks generation

requirements. They will also have the capability to support a larger portion of the

generation requirements but will require support generation from quick response

generators such as CCGT and hydro electric generators. Solar generation has the ability

to serve a large portion of load throughout the day. However it also requires support

from wind, CCGT and Hydro throughout the night time period. Thus is will be

44

fundamental to keep large conventional generators until large scale energy storage

solutions can be economically implemented.

Large scale energy storage would allow for the power system to comprise mostly of

intermittent renewable generators as excess power generated could be stored for times

of low generation. Renewable generation will hereby significantly reduce the emissions

produced by the power industry overall while also extending the lifespan of our natural

resources such as coal and gas.

45

5 Conclusion

In conclusion to this thesis it can be seen that the introduction of distributed and

renewable power generation can have significant benefits in electricity networks for

both market operators, generators and consumers. With the correct framework in place

the market operator will benefit as they will have a broader source of generation to

choose from. This will result in more complex operations needing to be performed by

the market operator, however it will increase the security of the electricity network

significantly. Also with the introduction of schemes such as RET and carbon pricing

placing mandatory renewable generation levels companies are starting to invest more

money into the construction of renewable generation sources. The main benefit for

consumers will be in the pricing of electricity. As more DG is placed throughout the

network the electricity price should reduce but also remain relatively stable due to the

large amount of generation.

The results of the simulations performed in conjunction with this thesis agree to that of

the results of other market simulations performed from an electricity market

perspective. The simulation results of this thesis show that it is beneficial to implement

DG and both large and small scale renewable generation into the electricity network. It

also shows that it is best to spread the implementation of DG in order to maximise its

effect on the electricity price.

The research performed for this thesis in conjunction with the results indicate that

allowing distributed and renewable generators to participate in the spot market creates

an incentive for large companies to invest in these technologies. With convention

generation having high emissions output and a finite fuel source it is in the best interest

of the NEM to become less dependent on these technologies and more dependent on

renewable and distributed generation.

46

6 Future Work

To further the research that has been under taken it is proposed that the simulations be

run on an existing network such as NSW or QLD. In order to implement distributed and

renewable generation optimisation algorithms will be used in order to maximise the

efficiency of the effect of DG. Once the ideal locations for DG have been identified

different types of DG will be implemented. This however will not be implemented as a

negative load as in these simulations. Instead the generator cost functions will be

implemented for the renewable sources. In order for the results to be liable existing

wind and solar predictions will be implemented as done so in this thesis. Since the

renewable generation cost functions rely on investment pay back cost, general figures

will be used as represented in other research.

The more advanced features of PLEXOS could be used to model the effects of distributed

generation on inter-regional trade of electricity as well as regional trade. To further the

PLEXOS model the state electricity networks of QLD, NSW, VIC, SA and TAS could be

designed and implemented into their regional nodes of the PLEXOS model. This however

would require all line, load and generator data for every state for it to be accurate. Most

of which is not freely available thus making it a very complex task to complete.

47

7 References

[1] AEMO, "National Transmission Network Development Plan," 2012. [Online].

Available: www.aemo.com.au~/media/Files/Other/ntndp/2012NTNDP.ashx 10

December 2012. [Accessed 2nd May 2013].

[2] Australian Energy Regulator, "State of the Energy Market," 20 Dec 2012.

[Online]. Available: http://www.aer.gov.au/node/18994. [Accessed 2nd May

2012].

[3] Climate Change Authority, "Renewable Energy Target Review - Final Report",

Commonwealth of Australia December 2012. [Online]. Available:

http://climatechangeauthority.gov.au/ret. [Accessed 4th May 2012].

[4] AEMO, "An Introduction to Australia's National Electricity Market", July 2010.

[Online]. Available: www.aemo.com, Document No:0000-0262.pdf. [Accessed

21st March 2013].

[5] BREE 2012, "Energy in Australia 2012", Canberra, February, Commonwealth of

Australia 2012 [Online].

Available:www.bree.gov.au/documents/publications/energy-in-

australia/energy-in-australia-2012.pdf [Accessed 18th March 2013]

[6] F. C. Schweppe and et. al., "Spot Pricing of Electricity", Massachusetts: Kluwer

Academic Publishers, 1988.

[7] R. D. Zimmerman and C. E. Murillo-Sanchez, "Matpower 4.1 User's Manual", 14

December 2011 [Online]. Available:

http://www.pserc.cornell.edu/matpower/manual.pdf [Accessed 10th May 2013]

[8] Energy Examplar, "PLEXOS for Power Systems", Ver.6.208 R05,

(Student license issued 11th April 2013),

[Online] Available: http://www.energyexemplar.com/

[9] P. Sorensen and et. al., "Power Fluctuations From Large Wind Farms," Power

systems, IEEE Transactions on, vol.22, no.3, pp.958,965, August 2007 [Online].

48

[10] N. Augustine and et. al., "Economic dispatch for a microgrid considering

renewable energy cost functions," Innovative Smart Grid Technologies (ISGT),

2012 IEEE PES, vol., no., pp.1,7, 16-20 Jan. 2012 [Online].

[11] AEMO, "Treatment of dispatchable loads in NEM", [Online] Available.

http://www.aemo.com

[12] T. Ackermann and et. al., "Electricity market regulations and their impact on

distributed generation," Electric Utility Deregulation and Restructuring and

Power Technologies, 2000. Proceedings. DRPT 2000. International Conference

on, vol., no., pp.608, 613, 2000 [Online].

[13] Australian Energy Market Commission, "National Electricity Rules Version 55",

7th March 2013. [Online] Available:

http://www.aemc.gov.au/Electricity/National-Electricity-Rules/Current-

Rules.html. [Accessed 25th April 2013].

[14] AEMO 2009, "Minimising Barriers to Cost-Effective Small Generator Participant

in the NEM", Discussion Paper Doc No: MD_SG_001 v1.0, [Online].

[15] H. Louie and K. Strunz, "Energy Market-Integrative Wind Plant Modeling for

Wind Plant Integration Economic Analysis", IEEE 2008, [Online].

[16] K. N. Hasan and et.al, "Renewable power penetration to remote grid -

transmission configuration and net benefit analyses," Innovative Smart Grid

Technologies Asia (ISGT), 2011 IEEE PES , vol., no., pp.1,8, 13-16 Nov. 2011

[Online].

[17] H. Huang and L. Fangxing, "Bidding Strategy for Wind Generation Considering

Conventional Generation and Transmission Constraints"

[18] X. Liu, “NEM Modelloing Report”, Modelling of Australian Electricity Market in

PLEXOS Software, University of Sydney, NSW 2006, Australia

[19] ACIL Tasman, “Final Report ‘Fuel resource, new entry and generation costs in the

NEM’,” Economics Policy Stratergy. 2009.

49

[20] S. Wong and et.al, " Coordination of Investor-Owned DG Capacity Growth in

Distribution Systems, IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 25, NO. 3,

AUGUST 2010

[21] Oz-Energy-Analysis, "Solar Irradiance Data", [Online], Available at:

http://www.oz-energy-analysis.org/data/irradiance.php, [Accessed 18th

September 2013]

[22] Oz-Energy-Analysis, "Wind Speed Data", [Online], Available at: http://www.oz-

energy-analysis.org/data/BoM_wind_data.php, [Accessed 18th September

2013]

[23] AEMO, "2012NTNDP_PlexosDatabase", Online, available at:

www.aemo.com.au~/media/Files/Other/ntndp/2012NTNDP_PlexosDatabase.as

hx 6 December 2012, [Accessed 23 July 2013]

[24] B. Elliston and et. al, "Least cost 100% renewable electricity scenarios in the

Australain National Electricity Matket", University of New South Wales, Sydney,

Australia, 21st March 2013

[25] M. Hindsberger and M. Eastwood, "Assessing the Market benefits of Large-scale

interconnectors", A case study from the National Electricity Market (NEM), IAEE

Conference, 19-23 June 2011.

[26] "Decision Adopting Energy Storage Procurement Framework and Design

Program",Before te public utilities comission of the state of California, Online

http://docs.cpuc.ca.gov/PublishedDocs/Published/G000/M078/K518/78518291.

PDF, [Accessed 17th October 2013]

[27] B. Wasowicz and et. al, "Evaluating regulatory and market frameworks for

energy storage deployment in electricity grids with high renewable energy

penetration", Institutefro High Voltage Engineering, RWTH Aachen University

E.ON New Build & Technology GmbH, 2012, Online.

[28] A. P. Agalgaonkar and et.al, “Placement and Penetration of Distributed

Generation under Standard Market Design”, International Journal of Emerging

Electrical Power Systems, Volume 1, Issue 1, 2004.

50

Appendix A.1 – Project Plan and Specifications

53

Appendix A.2 – Spring Session Gantt Chart

54

Appendix B.1 – Log Book Signature Sheet

55

Appendix C.1 – NEM Load Data, 1st January 2009

Regions

Period NSW QLD VIC SA TAS

1 7535 5611.54 4799.87 1310.89 909.71

2 7229.24 5457.34 4646.21 1272.69 896.63

3 6857.62 5294.12 4950.16 1178.87 897.52

4 6535.05 5153.47 4755.46 1130.78 906.22

5 6287.88 5060.33 4545.67 1059.53 893.19

6 6114.88 4983.49 4344.02 1010.64 891.48

7 5997.31 4899.56 4193.49 956.36 890.4

8 5992.67 4819.52 4091.29 951.95 887.37

9 5982.61 4825.31 4062.91 933.76 885.54

10 5916.82 4804.69 4021.12 917.82 892.07

11 5934.37 4779.16 4014.03 947.51 900.44

12 6000.91 4805.6 4033.43 941.87 892.38

13 6149.65 4939.64 4138.82 935.83 915.41

14 6377.8 5112.59 4218.4 975.19 945.1

15 6684.26 5384.87 4159.86 993.62 962.84

16 6919.82 5614.19 4239.08 950.32 1004.95

17 7250.55 5908.48 4305.49 965.13 1026.78

18 7513.67 6156.83 4410.89 976.47 1059.56

19 7740.55 6441.89 4510.2 1031.2 1044.55

20 7985.51 6712.97 4548.07 1037.83 1050.76

21 8113.57 6937.07 4578.3 1085.16 982.95

22 8297.23 7122.29 4637.86 1073.32 1038.19

23 8437.99 7209.98 4684.6 1079.18 1036.24

24 8543.12 7342.12 4652.27 1095.8 1023.93

25 8657.24 7425.06 4608.75 1097.17 1020.24

26 8734.24 7528.04 4588.08 1056.4 1004.59

27 8877.65 7597.42 4559.5 1091.54 1003.65

28 8975.66 7615.02 4546.08 1102.42 990.26

29 9077.81 7606.94 4547.51 1113.01 1004.42

30 9146.68 7605.56 4574.81 1108.26 1011.74

31 9223.64 7636.32 4615.78 1068.41 1018.04

32 9300.03 7606 4658.68 1073.51 1022.86

33 9363.53 7577.35 4723.2 1072.05 1046.26

34 9428.12 7533.54 4727.09 1090.55 1071.88

35 9376.06 7528.39 4726.05 1102.58 1075.22

36 9346.88 7470.04 4718.51 1104.78 1068.59

37 9158.63 7340.86 4676.96 1084.35 1060.69

38 9019.42 7341.39 4623.39 1085.32 1053.44

39 8959.04 7421.15 4606.23 1085.09 1065.58

40 9016.91 7359.89 4698.32 1098.67 1081.47

41 8849.1 7225.92 4828.65 1144.3 1092.04

42 8545.59 7017.67 4825.57 1164.27 1091.31

43 8392.97 6921.43 4702.29 1153.57 1064.84

44 8073.76 6596.84 4551.8 1140.68 1028.44

45 7993.97 6298.5 4471.27 1117.42 986.04

46 7743.54 6097.28 4414.34 1150.04 952.53

47 7511.03 5913.97 4755.9 1118.55 933.39

48 7297.68 5610.26 4687.9 1348.26 910.17

56

Appendix C.2 –PLEXOS Generator Data

Catego

ry

Gen

erator

No

des

Un

its

Max C

apacity

(MW

)

Min

Stable Level

(MW

)

Fuel P

rice ($/G

J)

Heat R

ate

(GJ/M

Wh

)

VO

&M

Ch

arge

($/M

Wh

)

Max R

amp

Up

P

enalty ($

/MW

)

Max R

amp

Do

wn

Pen

alty ($/M

W)

Au

x Incr (%

)

FO&

M C

harge

($/kW

/year)

NSW

Black C

oal

Bayswater NSWn 1 264

0 1240 1.29 10 1.19 100000 100000 6 49000

Eraring NSWn 1 264

0 920 1.72 10.2 1.19 100000 100000 6.5 49000

Liddel NSWn 1 200

0 1040 1.29 10.7 1.19 100000 100000 5 52000

Mt Piper NSWn 1 132

0 560 1.8 9.73 1.32 100000 100000 5 49000

Munmorah NSWn 1 600 270 1.75 11.7 1.19 100000 100000 7.3 55000

Redbank NSWn 1 150 67.5 1.01 12.3 1.19 100000 100000 8 49500

Vales Point B NSWn 1 132

0 594 1.75 10.2 1.19 100000 100000 4.6 49000

Wallerawang C NSWn 1 100

0 450 1.8 10.9 1.32 100000 100000 7.3 52000

NSW

Natu

ral Gas

Colongra NSWn 1 664 298.8 7.42 11.3 10.1 100000 100000 3 13000

Hunter Valley GT NSWn 1 50 22.5 30 12.9 9.61 100000 100000 3 13000

Smithfield NSWn 1 176 79.2 4.19 8.78 2.4 100000 100000 5 25000

Tallawarra NSWn 1 435 195.75 3.8 7.2 1.05 100000 100000 3 31000

Uranquinty NSWn 1 664 298.8 6.22 11.3 10.1 100000 100000 3 13000

QLD

Black C

oal

Callide B QLDn 1 700 315 1.32 9.97 1.2 100000 100000 7 49500

Callide C QLDn 1 840 378 1.32 9.47 1.2 100000 100000 4.8 49500

Collinsville QLDn 1 195 87.75 2.1 13 1.32 100000 100000 8 65000

Gladstone QLDn 1 168

0 756 1.56 10.2 1.19 100000 100000 5 52000

Kogan Creek QLDn 1 781 351.45 0.75 9.6 1.25 100000 100000 8 48000

Millmerran QLDn 1 852 383.4 0.85 9.6 1.19 100000 100000 4.5 48000

Stanwell QLDn 1 140

0 630 1.4 9.89 1.19 100000 100000 7 49000

Swanbank B QLDn 1 500 225 2.2 11.8 1.19 100000 100000 8 55000

Tarong QLDn 1 140

0 630 1.01 9.94 1.43 100000 100000 8 49500

Tarong North QLDn 1 450 202.5 1.01 9.18 1.43 100000 100000 5 48000

QLD

Natu

ral Gas

Barcaldine QLDn 1 57 25.65 6.67 9 2.4 100000 100000 3 25000

Braemar QLDn 1 504 226.8 2.67 12 7.93 100000 100000 2.5 13000

Braemar 2 QLDn 1 504 226.8 2.89 12 7.93 100000 100000 2.5 13000

Condamine A QLDn 1 135 61.75 0.95 7.5 1.05 100000 100000 3 31000

Darling Downs QLDn 1 630 283.5 3.41 7.83 1.05 100000 100000 6 31000

Oakey QLDn 1 282 126.9 4.24 11 9.61 100000 100000 3 13000

Roman GT QLDn 1 80 36 4.7 12 9.61 100000 100000 3 13000

Swanbank E QLDn 1 385 173.25 3.53 7.66 1.05 100000 100000 3 31000

Townswille QLDn 1 247 111.15 4.05 7.83 5.09 100000 100000 3 31000

57

Yarwun QLDn 1 160 72 3.55 10.6 0 100000 100000 2 25000

QLD

Fuel O

il Mackay GT QLDn 1 30 13.5 30 12.9 9.05 100000 100000 3 13000

QLD

Kero

sene

Mt Stuart GT QLDn 1 418 118.1 30 12 9.05 100000 100000 3 13000 SA D

iesel Angaston SAn 1 50 22.5 30 13.9 9.61 100000 100000 2.5 13000

SA N

atural G

as

Dry Creek GT SAn 1 156 70.65 4.72 13.9 9.61 100000 100000 3 13000

Ladbroke Grove SAn 1 80 36 5.05 12 3.6 100000 100000 3 13000

Mintaro GT SAn 1 90 40.5 6.61 12.9 9.61 100000 100000 3 13000

Osborne SAn 1 180 81 4.14 8.57 5.09 100000 100000 5 25000

Pelican Point SAn 1 478 215.1 3.98 7.5 1.05 100000 100000 2 31000

Quarantine SAn 1 216 97.2 5.98 11.3 9.61 100000 100000 5 13000

Torrens Island A SAn 1 480 216 4.04 13 2.26 100000 100000 5 40000

Torrens Island B SAn 1 800 360 4.04 12 2.26 100000 100000 5 40000

SA D

istillate

Hallet SAn 1 221 99.45 6.61 15 9.61 100000 100000 2.5 13000

Port Lincoln SAn 1 50 22.5 30 13.9 9.61 100000 100000 8 13000

Snuggery SAn 1 63 28.35 30 13.9 9.61 100000 100000 3 13000

SA

Bro

wn

C

oal

Northern SAn 1 530 238.5 1.52 10.3 1.19 100000 100000 5 55000

Playford B SAn 1 240 108 1.52 16.4 3 100000 100000 8 70000

TAS N

atural G

as

Bell Bay TASn 1 240 108 5.52 11.3 7.93 100000 100000 5 40000

Bell Bay Three TASn 1 105 47.25 5.52 12.4 7.93 100000 100000 2.5 13000

Tamar Valley TASn 1 200 90 5.52 7.5 1.05 100000 100000 3 31000

Tamar Valley OCGT TASn 1 75 33 5.52 12.4 7.93 100000 100000 2.5 13000

VIC

Bro

wn

Co

al Anglesea VICn 1 150 67.5 0.4 13.2 1.19 100000 100000 10 81000

Energy Brix VICn 1 195 87.75 0.6 15 1.19 100000 100000 15 60000

Hazelwood VICn 1 160

0 720 0.08 16.4 1.19 100000 100000 10 84030

Loy Yang A VICn 1 212

0 954 0.08 13.2 1.19 100000 100000 9 79000

Loy Yang B VICn 1 100

0 450 0.37 13.5 1.19 100000 100000 7.5 51200

Yallourn VICn 1 148

0 666 0.1 15.3 1.19 100000 100000 8.9 82400

VIC

Natu

ral Gas

Bairnsdale VICn 1 94 42.3 4.29 10.6 2.26 100000 100000 3 13000

Jeeralang A VICn 1 204 91.8 3.88 15.7 9.05 100000 100000 3 13000

Jeeralang B VICn 1 228 102.6 3.88 15.7 9.05 100000 100000 3 13000

Laverton VICn 1 312 140.4 4.11 11.8 7.93 100000 100000 2.5 13000

Mortlake VICn 1 550 247.5 5 11.3 8.33 100000 100000 3 13000

Newport VICn 1 500 225 4.08 10.8 2.26 100000 100000 5 40000

Somerton VICn 1 160 72 4.12 15 9.61 100000 100000 2.5 13000

Valley Power VICn 1 300 135 3.87 15 9.61 100000 100000 3 13000

58

Appendix C.3 – Matlab NEM Function

function mpc = case5_NEM_MC( Q_Load, N_Load, V_Load, S_Load, T_Load ) %case5_NEM %% MATPOWER Case Format : Version 2 mpc.version = '2'; %%----- Power Flow Data -----%% %% system MVA base mpc.baseMVA = 100; %% bus data % bus_i type Pd Qd Gs Bs area Vm Va baseKV zone Vmax Vmin mpc.bus = [ 1 2 Q_Load 0 0 0 1 1.04 0 0 1 1.06 0.94; 2 3 N_Load 0 0 0 1 1.04 0 0 1 1.06 0.94; 3 2 V_Load 0 0 0 1 1.04 0 0 1 1.06 0.94; 4 1 S_Load 0 0 0 1 1.04 0 0 1 1.06 0.94; 5 1 T_Load 0 0 0 1 1.04 0 0 1 1.06 0.94; ]; %% generator data % bus Pg Qg Qmax Qmin Vg mBase status Pmax Pmin Pc1 Pc2 Qc1min Qc1max Qc2min Qc2max ramp_agc ramp_10 ramp_30 ramp_q apf mpc.gen = [ 1 57 0 17.69463984 0 1.04 100 1 71.25 0 0 0 0 0 0 0 0 0 0 0 0; 1 504 0 156.4578681 0 1.04 100 1 630 0 0 0 0 0 0 0 0 0 0 0 0; 1 504 0 156.4578681 0 1.04 100 1 630 0 0 0 0 0 0 0 0 0 0 0 0; 1 700 0 217.3025946 0 1.04 100 1 875 0 0 0 0 0 0 0 0 0 0 0 0; 1 840 0 260.7631135 0 1.04 100 1 1050 0 0 0 0 0 0 0 0 0 0 0 0; 1 195 0 60.5342942 0 1.04 100 1 243.75 0 0 0 0 0 0 0 0 0 0 0 0; 1 135 0 41.90835752 0 1.04 100 1 168.75 0 0 0 0 0 0 0 0 0 0 0 0; 1 630 0 195.5723351 0 1.04 100 1 787.5 0 0 0 0 0 0 0 0 0 0 0 0; 1 1680 0 521.5262269 0 1.04 100 1 2100 0 0 0 0 0 0 0 0 0 0 0 0; 1 781 0 242.4476091 0 1.04 100 1 976.25 0 0 0 0 0 0 0 0 0 0 0 0; 1 30 0 9.312968338 0 1.04 100 1 37.5 0 0 0 0 0 0 0 0 0 0 0 0; 1 852 0 264.4883008 0 1.04 100 1 1065 0 0 0 0 0 0 0 0 0 0 0 0; 1 418 0 129.7606922 0 1.04 100 1 522.5 0 0 0 0 0 0 0 0 0 0 0 0; 1 282 0 87.54190238 0 1.04 100 1 352.5 0 0 0 0 0 0 0 0 0 0 0 0; 1 80 0 24.83458223 0 1.04 100 1 100 0 0 0 0 0 0 0 0 0 0 0 0; 1 1400 0 434.6051891 0 1.04 100 1 1750 0 0 0 0 0 0 0 0 0 0 0 0; 1 500 0 155.216139 0 1.04 100 1 625 0 0 0 0 0 0 0 0 0 0 0 0; 1 385 0 119.516427 0 1.04 100 1 481.25 0 0 0 0 0 0 0 0 0 0 0 0; 1 1400 0 434.6051891 0 1.04 100 1 1750 0 0 0 0 0 0 0 0 0 0 0 0; 1 450 0 139.6945251 0 1.04 100 1 562.5 0 0 0 0 0 0 0 0 0 0 0 0; 1 247 0 76.67677265 0 1.04 100 1 308.75 0 0 0 0 0 0 0 0 0 0 0 0; 1 160 0 49.66916447 0 1.04 100 1 200 0 0 0 0 0 0 0 0 0 0 0 0; 2 2640 0 819.5412137 0 1.04 100 1 3300 0 0 0 0 0 0 0 0 0 0 0 0; 2 664 0 206.1270325 0 1.04 100 1 830 0 0 0 0 0 0 0 0 0 0 0 0; 2 2640 0 819.5412137 0 1.04 100 1 3300 0 0 0 0 0 0 0 0 0 0 0 0; 2 50 0 15.5216139 0 1.04 100 1 62.5 0 0 0 0 0 0 0 0 0 0 0 0; 2 2000 0 620.8645559 0 1.04 100 1 2500 0 0 0 0 0 0 0 0 0 0 0 0; 2 1320 0 409.7706069 0 1.04 100 1 1650 0 0 0 0 0 0 0 0 0 0 0 0; 2 600 0 186.2593668 0 1.04 100 1 750 0 0 0 0 0 0 0 0 0 0 0 0; 2 150 0 46.56484169 0 1.04 100 1 187.5 0 0 0 0 0 0 0 0 0 0 0 0; 2 176 0 54.63608092 0 1.04 100 1 220 0 0 0 0 0 0 0 0 0 0 0 0; 2 435 0 135.0380409 0 1.04 100 1 543.75 0 0 0 0 0 0 0 0 0 0 0 0; 2 664 0 206.1270325 0 1.04 100 1 830 0 0 0 0 0 0 0 0 0 0 0 0; 2 1320 0 409.7706069 0 1.04 100 1 1650 0 0 0 0 0 0 0 0 0 0 0 0; 2 1000 0 310.4322779 0 1.04 100 1 1250 0 0 0 0 0 0 0 0 0 0 0 0; 3 150 0 46.56484169 0 1.04 100 1 187.5 0 0 0 0 0 0 0 0 0 0 0 0; 3 94 0 29.18063413 0 1.04 100 1 117.5 0 0 0 0 0 0 0 0 0 0 0 0; 3 195 0 60.5342942 0 1.04 100 1 243.75 0 0 0 0 0 0 0 0 0 0 0 0; 3 1600 0 496.6916447 0 1.04 100 1 2000 0 0 0 0 0 0 0 0 0 0 0 0;

59

3 204 0 63.3281847 0 1.04 100 1 255 0 0 0 0 0 0 0 0 0 0 0 0; 3 228 0 70.77855937 0 1.04 100 1 285 0 0 0 0 0 0 0 0 0 0 0 0; 3 312 0 96.85487071 0 1.04 100 1 390 0 0 0 0 0 0 0 0 0 0 0 0; 3 2120 0 658.1164292 0 1.04 100 1 2650 0 0 0 0 0 0 0 0 0 0 0 0; 3 1000 0 310.4322779 0 1.04 100 1 1250 0 0 0 0 0 0 0 0 0 0 0 0; 3 550 0 170.7377529 0 1.04 100 1 687.5 0 0 0 0 0 0 0 0 0 0 0 0; 3 500 0 155.216139 0 1.04 100 1 625 0 0 0 0 0 0 0 0 0 0 0 0; 3 160 0 49.66916447 0 1.04 100 1 200 0 0 0 0 0 0 0 0 0 0 0 0; 3 300 0 93.12968338 0 1.04 100 1 375 0 0 0 0 0 0 0 0 0 0 0 0; 3 1480 0 459.4397713 0 1.04 100 1 1850 0 0 0 0 0 0 0 0 0 0 0 0; 4 50 0 15.5216139 0 1.04 100 1 62.5 0 0 0 0 0 0 0 0 0 0 0 0; 4 156 0 48.42743536 0 1.04 100 1 195 0 0 0 0 0 0 0 0 0 0 0 0; 4 221 0 68.60553342 0 1.04 100 1 276.25 0 0 0 0 0 0 0 0 0 0 0 0; 4 80 0 24.83458223 0 1.04 100 1 100 0 0 0 0 0 0 0 0 0 0 0 0; 4 90 0 27.93890501 0 1.04 100 1 112.5 0 0 0 0 0 0 0 0 0 0 0 0; 4 530 0 164.5291073 0 1.04 100 1 662.5 0 0 0 0 0 0 0 0 0 0 0 0; 4 180 0 55.87781003 0 1.04 100 1 225 0 0 0 0 0 0 0 0 0 0 0 0; 4 478 0 148.3866289 0 1.04 100 1 597.5 0 0 0 0 0 0 0 0 0 0 0 0; 4 240 0 74.5037467 0 1.04 100 1 300 0 0 0 0 0 0 0 0 0 0 0 0; 4 50 0 15.5216139 0 1.04 100 1 62.5 0 0 0 0 0 0 0 0 0 0 0 0; 4 216 0 67.05337203 0 1.04 100 1 270 0 0 0 0 0 0 0 0 0 0 0 0; 4 63 0 19.55723351 0 1.04 100 1 78.75 0 0 0 0 0 0 0 0 0 0 0 0; 4 480 0 149.0074934 0 1.04 100 1 600 0 0 0 0 0 0 0 0 0 0 0 0; 4 800 0 248.3458223 0 1.04 100 1 1000 0 0 0 0 0 0 0 0 0 0 0 0; 5 240 0 74.5037467 0 1.04 100 1 300 0 0 0 0 0 0 0 0 0 0 0 0; 5 105 0 32.59538918 0 1.04 100 1 131.25 0 0 0 0 0 0 0 0 0 0 0 0; 5 75 0 23.28242084 0 1.04 100 1 93.75 0 0 0 0 0 0 0 0 0 0 0 0; 5 200 0 62.08645559 0 1.04 100 1 250 0 0 0 0 0 0 0 0 0 0 0 0; ]; %% branch data % fbus tbus r x b rateA rateB rateC ratio angle status angmin angmax mpc.branch = [ 1 2 0.01 0.002 0 220 0 0 0 0 1 -360 360; 1 2 0.01 0.002 0 1000 0 0 0 0 1 -360 360; 3 2 0.01 0.002 0 900 0 0 0 0 1 -360 360; 3 4 0.01 0.002 0 650 0 0 0 0 1 -360 360; 3 4 0.01 0.002 0 220 0 0 0 0 1 -360 360; 3 5 0.01 0.002 0 480 0 0 0 0 1 -360 360; ]; %%----- OPF Data -----%% %% generator cost data % 1 startup shutdown n x1 y1 ... xn yn % 2 startup shutdown n c(n-1) ... c0 mpc.gencost = [ 2 0 0 3 0.0667 11.385 0; 2 0 0 3 0.0267 15.18 0; 2 0 0 3 0.0289 15.18 0; 2 0 0 3 0.0132 12.61205 0; 2 0 0 3 0.0132 11.97955 0; 2 0 0 3 0.021 16.445 0; 2 0 0 3 0.0095 9.4875 0; 2 0 0 3 0.0341 9.90495 0; 2 0 0 3 0.0156 12.94095 0; 2 0 0 3 0.0075 12.144 0; 2 0 0 3 0.3 16.2679 0; 2 0 0 3 0.0085 12.144 0; 2 0 0 3 0.3 15.18 0;

60

2 0 0 3 0.0424 13.9656 0; 2 0 0 3 0.047 15.18 0; 2 0 0 3 0.014 12.51085 0; 2 0 0 3 0.022 14.927 0; 2 0 0 3 0.0353 9.6899 0; 2 0 0 3 0.0101 12.5741 0; 2 0 0 3 0.0101 11.6127 0; 2 0 0 3 0.0405 9.90495 0; 2 0 0 3 0.0355 13.39635 0; 2 0 0 3 0.0129 12.68795 0; 2 0 0 3 0.0742 14.23125 0; 2 0 0 3 0.0172 12.86505 0; 2 0 0 3 0.3 16.2679 0; 2 0 0 3 0.0129 13.47225 0; 2 0 0 3 0.018 12.30845 0; 2 0 0 3 0.0175 14.78785 0; 2 0 0 3 0.0101 15.54685 0; 2 0 0 3 0.0419 11.1067 0; 2 0 0 3 0.038 9.108 0; 2 0 0 3 0.0622 14.23125 0; 2 0 0 3 0.0175 12.86505 0; 2 0 0 3 0.018 13.7632 0; 2 0 0 3 0.004 16.7486 0; 2 0 0 3 0.0429 13.39635 0; 2 0 0 3 0.006 18.975 0; 2 0 0 3 0.0008 20.6954 0; 2 0 0 3 0.0388 21.1508 0; 2 0 0 3 0.0388 19.8858 0; 2 0 0 3 0.0411 14.9776 0; 2 0 0 3 0.0008 16.7486 0; 2 0 0 3 0.0037 17.11545 0; 2 0 0 3 0.05 14.23125 0; 2 0 0 3 0.0408 13.67465 0; 2 0 0 3 0.0412 18.975 0; 2 0 0 3 0.0387 18.975 0; 2 0 0 3 0.001 19.3798 0; 2 0 0 3 0.3 17.5076 0; 2 0 0 3 0.0472 17.52025 0; 2 0 0 3 0.0661 18.975 0; 2 0 0 3 0.0505 15.18 0; 2 0 0 3 0.0661 16.2679 0; 2 0 0 3 0.0152 13.0548 0; 2 0 0 3 0.0414 10.84105 0; 2 0 0 3 0.0398 9.4875 0; 2 0 0 3 0.0152 20.7966 0; 2 0 0 3 0.3 17.52025 0; 2 0 0 3 0.0598 14.23125 0; 2 0 0 3 0.3 17.52025 0; 2 0 0 3 0.0404 16.4956 0; 2 0 0 3 0.0404 15.18 0; 2 0 0 3 0.0552 14.23125 0; 2 0 0 3 0.0552 15.69865 0; 2 0 0 3 0.0552 15.69865 0; 2 0 0 3 0.0552 9.4875 0; ]; end

61

Appendix D.1 – DG Penetration Data

DG Penetration

BUS P (MW)Cost 10% scaling factor 20% scaling factor 30% scaling factor

41 37 41.24768 29.17360595 0.068773234 34.91746 0.041156841 33.11266 0.026019691

44 16 41.12362 12.61561338 0.029739777 15.09944 0.017797553 14.31899 0.011251758

53 23 41.09188 18.13494424 0.042750929 21.70545 0.025583982 20.58354 0.016174402

52 18 41.01465 14.19256506 0.033457249 16.98687 0.020022247 16.10886 0.012658228

40 66 40.98641 52.0394052 0.122676580 62.28521 0.073414905 59.06582 0.046413502

45 53 40.94782 41.78921933 0.098513011 50.01691 0.058954394 47.43165 0.037271449

43 18 40.84167 14.19256506 0.033457249 16.98687 0.020022247 16.10886 0.012658228

39 27 40.82746 21.28884758 0.050185874 25.48031 0.030033370 24.16329 0.018987342

42 96 40.81989 75.6936803 0.178438662 90.59666 0.106785317 85.91392 0.067510549

29 24 40.80833 18.92342007 0.044609665 22.64917 0.026696329 21.47848 0.016877637

58 12 40.79386 9.461710037 0.022304833 11.32458 0.013348165 10.73924 0.008438819

112 68 40.72965 53.61635688 0.126394052 64.17264 0.075639600 60.8557 0.047819972

31 43 40.70931 33.90446097 0.079925651 40.57976 0.047830923 38.48228 0.0302391

117 20 40.68417 15.76951673 0.037174721 18.8743 0.022246941 17.89873 0.014064698

51 17 40.659 13.40408922 0.031598513 16.04316 0.018909900 15.21392 0.011954993

56 84 40.6512 79.27208 0.093437152 75.17468 0.05907173

55 63 40.64268 59.45406 0.070077864 56.38101 0.044303797

54 113 40.64263 106.6398 0.125695217 101.1278 0.079465541

13 34 40.63961 32.08632 0.037819800 30.42785 0.023909986

28 17 40.60349 16.04316 0.018909900 15.21392 0.011954993

107 50 40.58055 47.18576 0.055617353 44.74684 0.035161744

20 18 40.54082 16.10886 0.012658228

1 51 40.52969 45.64177 0.035864979

33 23 40.51381 20.58354 0.016174402

2 20 40.49023 17.89873 0.014064698

76 68 40.45706 60.8557 0.047819972

118 33 40.43716 29.53291 0.023206751

19 45 40.43163 40.27215 0.03164557

57 12 40.42865 10.73924 0.008438819

115 22 40.42312 19.68861 0.015471167

15 90 40.32627 80.5443 0.063291139

114 8 40.30152 7.159494 0.005625879

14 14 40.27677 12.52911 0.009845288

21 14 40.25202 12.52911 0.009845288

74 68 40.22727 60.8557 0.047819972

3 39 40.30242 34.90253 0.02742616

Rankerd Cost(Descending)

62

Appendix D.2 – 48 Period Solar Irradiance Data, 1st January 2009

Period 1 2 3 4 5 6 7 8 9 10 11 12 irradiance

value 0 0 0 0 0 0 0 0 1 2 4 8


value 11 14 15 16 17 18 19 20 21 22 23 23


value 24 23 22 21 20 20 18 17 16 15 14 12


value 8 4 2 1 0 0 0 0 0 0 0 0

63

Appendix D.3 – “30% Solar” Matlab Bus Data

%% bus data % bus_i type Pd Qd Gs Bs area Vm Va baseKV zone Vmax

Vmin mpc.bus = [ 1 2 51-0.035864979*Psolar 27 0 0 1 0.955 10.67 138 1

1.06 0.94; 2 1 20-0.014064698*Psolar 9 0 0 1 0.971 11.22 138 1

1.06 0.94; 3 1 39-0.02742616*Psolar 10 0 0 1 0.968 11.56 138 1

1.06 0.94; 4 2 39 12 0 0 1 0.998 15.28 138 1 1.06 0.94; 5 1 0 0 0 -40 1 1.002 15.73 138 1 1.06 0.94; 6 2 52 22 0 0 1 0.99 13 138 1 1.06 0.94; 7 1 19 2 0 0 1 0.989 12.56 138 1 1.06 0.94; 8 2 28 0 0 0 1 1.015 20.77 345 1 1.06 0.94; 9 1 0 0 0 0 1 1.043 28.02 345 1 1.06 0.94; 10 2 0 0 0 0 1 1.05 35.61 345 1 1.06 0.94; 11 1 70 23 0 0 1 0.985 12.72 138 1 1.06 0.94; 12 2 47 10 0 0 1 0.99 12.2 138 1 1.06 0.94; 13 1 34-0.023909986*Psolar 16 0 0 1 0.968 11.35 138 1

1.06 0.94; 14 1 14-0.009845288*Psolar 1 0 0 1 0.984 11.5 138 1

1.06 0.94; 15 2 90-0.063291139*Psolar 30 0 0 1 0.97 11.23 138 1

1.06 0.94; 16 1 25 10 0 0 1 0.984 11.91 138 1 1.06 0.94; 17 1 11 3 0 0 1 0.995 13.74 138 1 1.06 0.94; 18 2 60 34 0 0 1 0.973 11.53 138 1 1.06 0.94; 19 2 45-0.03164557*Psolar 25 0 0 1 0.963 11.05 138 1

1.06 0.94; 20 1 18-0.012658228*Psolar 3 0 0 1 0.958 11.93 138 1

1.06 0.94; 21 1 14-0.009845288*Psolar 8 0 0 1 0.959 13.52 138 1

1.06 0.94; 22 1 10 5 0 0 1 0.97 16.08 138 1 1.06 0.94; 23 1 7 3 0 0 1 1 21 138 1 1.06 0.94; 24 2 13 0 0 0 1 0.992 20.89 138 1 1.06 0.94; 25 2 0 0 0 0 1 1.05 27.93 138 1 1.06 0.94; 26 2 0 0 0 0 1 1.015 29.71 345 1 1.06 0.94; 27 2 71 13 0 0 1 0.968 15.35 138 1 1.06 0.94; 28 1 17-0.011954993*Psolar 7 0 0 1 0.962 13.62 138 1

1.06 0.94; 29 1 24-0.016877637*Psolar 4 0 0 1 0.963 12.63 138 1

1.06 0.94; 30 1 0 0 0 0 1 0.968 18.79 345 1 1.06 0.94; 31 2 43-0.030239100*Psolar 27 0 0 1 0.967 12.75 138 1

1.06 0.94; 32 2 59 23 0 0 1 0.964 14.8 138 1 1.06 0.94; 33 1 23-0.016174402*Psolar 9 0 0 1 0.972 10.63 138 1

1.06 0.94; 34 2 59 26 0 14 1 0.986 11.3 138 1 1.06 0.94; 35 1 33 9 0 0 1 0.981 10.87 138 1 1.06 0.94; 36 2 31 17 0 0 1 0.98 10.87 138 1 1.06 0.94; 37 1 0 0 0 -25 1 0.992 11.77 138 1 1.06 0.94; 38 1 0 0 0 0 1 0.962 16.91 345 1 1.06 0.94; 39 1 27-0.018987342*Psolar 11 0 0 1 0.97 8.41 138 1

1.06 0.94;

64

40 2 66-0.046413502*Psolar 23 0 0 1 0.97 7.35 138 1

1.06 0.94; 41 1 37-0.026019691*Psolar 10 0 0 1 0.967 6.92 138 1

1.06 0.94; 42 2 96-0.067510549*Psolar 23 0 0 1 0.985 8.53 138 1

1.06 0.94; 43 1 18-0.012658228*Psolar 7 0 0 1 0.978 11.28 138 1

1.06 0.94; 44 1 16-0.011251758*Psolar 8 0 10 1 0.985 13.82 138 1

1.06 0.94; 45 1 53-0.037271449*Psolar 22 0 10 1 0.987 15.67 138 1

1.06 0.94; 46 2 28 10 0 10 1 1.005 18.49 138 1 1.06 0.94; 47 1 34 0 0 0 1 1.017 20.73 138 1 1.06 0.94; 48 1 20 11 0 15 1 1.021 19.93 138 1 1.06 0.94; 49 2 87 30 0 0 1 1.025 20.94 138 1 1.06 0.94; 50 1 17 4 0 0 1 1.001 18.9 138 1 1.06 0.94; 51 1 17-0.011954993*Psolar 8 0 0 1 0.967 16.28 138 1

1.06 0.94; 52 1 18-0.012658228*Psolar 5 0 0 1 0.957 15.32 138 1

1.06 0.94; 53 1 23-0.016174402*Psolar 11 0 0 1 0.946 14.35 138 1

1.06 0.94; 54 2 113-0.079465541*Psolar 32 0 0 1 0.955 15.26 138 1

1.06 0.94; 55 2 63-0.044303797*Psolar 22 0 0 1 0.952 14.97 138 1

1.06 0.94; 56 2 84-0.05907173*Psolar 18 0 0 1 0.954 15.16 138 1

1.06 0.94; 57 1 12-0.008438819*Psolar 3 0 0 1 0.971 16.36 138 1

1.06 0.94; 58 1 12-0.008438819*Psolar 3 0 0 1 0.959 15.51 138 1

1.06 0.94; 59 2 277 113 0 0 1 0.985 19.37 138 1 1.06 0.94; 60 1 78 3 0 0 1 0.993 23.15 138 1 1.06 0.94; 61 2 0 0 0 0 1 0.995 24.04 138 1 1.06 0.94; 62 2 77 14 0 0 1 0.998 23.43 138 1 1.06 0.94; 63 1 0 0 0 0 1 0.969 22.75 345 1 1.06 0.94; 64 1 0 0 0 0 1 0.984 24.52 345 1 1.06 0.94; 65 2 0 0 0 0 1 1.005 27.65 345 1 1.06 0.94; 66 2 39 18 0 0 1 1.05 27.48 138 1 1.06 0.94; 67 1 28 7 0 0 1 1.02 24.84 138 1 1.06 0.94; 68 1 0 0 0 0 1 1.003 27.55 345 1 1.06 0.94; 69 3 0 0 0 0 1 1.035 30 138 1 1.06 0.94; 70 2 66 20 0 0 1 0.984 22.58 138 1 1.06 0.94; 71 1 0 0 0 0 1 0.987 22.15 138 1 1.06 0.94; 72 2 12 0 0 0 1 0.98 20.98 138 1 1.06 0.94; 73 2 6 0 0 0 1 0.991 21.94 138 1 1.06 0.94; 74 2 68-0.047819972*Psolar 27 0 12 1 0.958 21.64 138 1

1.06 0.94; 75 1 47 11 0 0 1 0.967 22.91 138 1 1.06 0.94; 76 2 68-0.047819972*Psolar 36 0 0 1 0.943 21.77 138 1

1.06 0.94; 77 2 61 28 0 0 1 1.006 26.72 138 1 1.06 0.94; 78 1 71 26 0 0 1 1.003 26.42 138 1 1.06 0.94; 79 1 39 32 0 20 1 1.009 26.72 138 1 1.06 0.94; 80 2 130 26 0 0 1 1.04 28.96 138 1 1.06 0.94; 81 1 0 0 0 0 1 0.997 28.1 345 1 1.06 0.94; 82 1 54 27 0 20 1 0.989 27.24 138 1 1.06 0.94;

65

83 1 20 10 0 10 1 0.985 28.42 138 1 1.06 0.94; 84 1 11 7 0 0 1 0.98 30.95 138 1 1.06 0.94; 85 2 24 15 0 0 1 0.985 32.51 138 1 1.06 0.94; 86 1 21 10 0 0 1 0.987 31.14 138 1 1.06 0.94; 87 2 0 0 0 0 1 1.015 31.4 161 1 1.06 0.94; 88 1 48 10 0 0 1 0.987 35.64 138 1 1.06 0.94; 89 2 0 0 0 0 1 1.005 39.69 138 1 1.06 0.94; 90 2 163 42 0 0 1 0.985 33.29 138 1 1.06 0.94; 91 2 10 0 0 0 1 0.98 33.31 138 1 1.06 0.94; 92 2 65 10 0 0 1 0.993 33.8 138 1 1.06 0.94; 93 1 12 7 0 0 1 0.987 30.79 138 1 1.06 0.94; 94 1 30 16 0 0 1 0.991 28.64 138 1 1.06 0.94; 95 1 42 31 0 0 1 0.981 27.67 138 1 1.06 0.94; 96 1 38 15 0 0 1 0.993 27.51 138 1 1.06 0.94; 97 1 15 9 0 0 1 1.011 27.88 138 1 1.06 0.94; 98 1 34 8 0 0 1 1.024 27.4 138 1 1.06 0.94; 99 2 42 0 0 0 1 1.01 27.04 138 1 1.06 0.94; 100 2 37 18 0 0 1 1.017 28.03 138 1 1.06 0.94; 101 1 22 15 0 0 1 0.993 29.61 138 1 1.06 0.94; 102 1 5 3 0 0 1 0.991 32.3 138 1 1.06 0.94; 103 2 23 16 0 0 1 1.001 24.44 138 1 1.06 0.94; 104 2 38 25 0 0 1 0.971 21.69 138 1 1.06 0.94; 105 2 31 26 0 20 1 0.965 20.57 138 1 1.06 0.94; 106 1 43 16 0 0 1 0.962 20.32 138 1 1.06 0.94; 107 2 50-0.035161744*Psolar 12 0 6 1 0.952 17.53 138 1

1.06 0.94; 108 1 2 1 0 0 1 0.967 19.38 138 1 1.06 0.94; 109 1 8 3 0 0 1 0.967 18.93 138 1 1.06 0.94; 110 2 39 30 0 6 1 0.973 18.09 138 1 1.06 0.94; 111 2 0 0 0 0 1 0.98 19.74 138 1 1.06 0.94; 112 2 68-0.047819972*Psolar 13 0 0 1 0.975 14.99 138 1

1.06 0.94; 113 2 6 0 0 0 1 0.993 13.74 138 1 1.06 0.94; 114 1 8-0.005625879*Psolar 3 0 0 1 0.96 14.46 138 1

1.06 0.94; 115 1 22-0.015471167*Psolar 7 0 0 1 0.96 14.46 138 1

1.06 0.94; 116 2 184 0 0 0 1 1.005 27.12 138 1 1.06 0.94; 117 1 20-0.014064698*Psolar 8 0 0 1 0.974 10.67 138 1

1.06 0.94; 118 1 33-0.023206751*Psolar 15 0 0 1 0.949 21.92 138 1

1.06 0.94;

];

66

Appendix D.4 – Wind Speed Data

>Bo

M_0

39

32

2

(-

23

.52

93

,15

1.2

76

3)

RU

ND

LE ISLAN

D

>Bo

M_0

61

39

2

(-

31

.74

16

,15

0.7

93

7)

MU

RR

UR

UN

DI G

AP

AW

S

>Bo

M_0

70

21

7

(-

36

.29

39

,14

8.9

72

5) C

OO

MA

A

IRP

OR

T AW

S

>Bo

M_0

41

35

9

(-

27

.40

34

,15

1.7

41

3)

OA

KEY

AER

O

>Bo

M_0

21

13

3

(-

33

.76

76

,13

8.2

18

2)

SNO

WTO

WN

(R

AYV

ILLE

PA

RK

)

>Bo

M_0

22

04

6

(-

35

.11

21

,13

7.7

39

5)

EDITH

BU

RG

H

>Bo

M_0

23

87

5

(-

35

.56

95

,13

8.2

86

4)

PA

RA

WA

(SECO

ND

VA

LLEY

FOR

EST AW

S)

>Bo

M_0

26

02

1

(-

37

.74

73

,14

0.7

73

9) M

OU

NT

GA

MB

IER A

ERO

>Bo

M_0

47

04

8

(-

32

.00

12

,14

1.4

69

4)

BR

OK

EN H

ILL AIR

PO

RT A

WS

8.7 2.6 2.6 0.5 5.7 5.7 3.6 5.1 0

8.7 2.6 0 4.6 4.6 6.2 4.1 5.1 0

8.7 3.1 4.1 3.6 2.6 5.1 4.6 4.6 4.6

9.3 3.1 4.1 4.1 1 5.7 4.1 5.1 4.6

8.7 3.6 3.1 3.1 1.5 4.6 3.1 6.7 4.1

8.2 3.6 2.6 3.6 1.5 5.7 3.6 5.1 3.6

7.7 4.6 4.6 3.6 0 3.6 4.6 4.6 3.1

8.2 4.6 6.2 2.1 0 5.7 4.1 4.1 2.6

7.2 4.6 4.6 4.1 1.5 5.1 4.1 6.7 2.6

7.2 4.6 3.6 4.1 1.5 5.1 4.6 5.1 3.6

6.2 5.1 3.1 3.6 0 7.2 3.6 6.2 3.6

5.1 4.6 3.6 3.1 0 5.1 3.6 6.2 4.1

5.1 4.1 4.1 2.1 2.6 4.6 3.1 5.7 5.7

4.6 4.1 7.2 1.5 3.1 5.1 3.6 5.7 7.7

5.1 4.6 7.2 2.1 3.6 6.2 4.1 7.2 8.2

5.1 4.6 7.7 1 4.1 4.6 4.1 5.1 8.2

5.1 5.7 7.7 1.5 5.1 5.1 5.1 7.2 8.7

4.6 6.2 9.3 4.6 5.7 6.2 5.1 7.7 7.7

4.6 8.2 9.0 4.1 5.1 8.7 6.7 7.7 7.7

4.6 6.7 8.7 6.2 6.2 6.7 6.2 8.2 6.7

5.1 8.2 9.3 6.2 5.7 7.2 6.7 8.7 6.2

6.2 10.3 10.3 7.7 5.7 7.2 7.2 8.2 5.7

6.2 10.3 10.8 7.2 5.7 8.2 6.7 9.3 5.1

6.7 12.9 11.3 6.7 6.7 7.7 9.3 9.3 5.7

6.7 12.9 12.9 7.7 5.7 7.7 9.8 8.7 5.1

6.7 14.4 11.3 7.2 6.7 6.2 8.7 8.7 5.1

7.7 10.8 11.8 7.2 5.7 8.2 9.3 9.8 5.7

7.7 12.3 11.3 7.2 6.2 7.2 9.3 9.3 5.1

7.7 11.8 11.3 7.2 8.2 8.7 8.7 10.8 6.7

7.7 12.9 10.8 6.2 8.2 8.2 9.3 9.8 6.7

7.7 11.3 12.3 6.7 7.7 7.7 9.8 9.8 8.7

8.7 12.9 12.8 6.7 8.2 9.3 9.8 10.3 6.7

8.7 12.9 13.4 6.2 7.7 9.3 9.8 10.3 7.2

8.7 10.8 11.3 6.7 8.2 9.8 10.3 9.3 8.7

8.7 10.8 9.3 5.7 8.2 10.8 10.8 10.3 7.7

8.7 10.8 7.7 5.1 9.3 11.3 10.3 8.2 7.7

9.3 9.3 9.3 4.1 9.8 11.3 10.8 8.7 8.2

8.7 7.7 9.3 3.6 9.8 11.3 10.3 9.8 7.2

9.3 7.2 8.2 3.1 9.8 10.3 9.8 7.7 7.2

8.7 3.1 7.2 5.1 9.8 9.3 9.8 8.7 5.7

8.2 1 5.1 3.1 8.2 9.3 9.8 6.2 5.7

8.7 1.5 4.1 12.9 7.2 9.8 9.8 5.1 6.7

8.2 2.6 4.1 6.7 6.2 8.7 8.7 6.7 6.2

7.7 3.1 6.2 5.7 6.7 8.2 7.7 5.1 8.7

7.2 3.6 4.6 3.6 5.7 7.7 9.3 3.6 9.3

8.2 2.6 3.6 4.1 6.7 8.2 8.7 3.1 9.3

8.7 2.1 3.1 4.1 7.2 7.7 7.2 2.6 8.2

7.2 2.1 5.1 3.1 4.1 8.2 8.7 2.6 9.8

67

Appendix D.5 – “30% Wind” Matlab Bus Data

%% bus data % bus_i type Pd Qd Gs Bs area Vm Va baseKV zone Vmax

Vmin mpc.bus = [ 1 2 51-0.035864979*Pwind 27 0 0 1 0.955 10.67 138 1

1.06 0.94; 2 1 20-0.014064698*Pwind 9 0 0 1 0.971 11.22 138 1

1.06 0.94; 3 1 39-0.02742616*Pwind 10 0 0 1 0.968 11.56 138 1 1.06

0.94; 4 2 39 12 0 0 1 0.998 15.28 138 1 1.06 0.94; 5 1 0 0 0 -40 1 1.002 15.73 138 1 1.06 0.94; 6 2 52 22 0 0 1 0.99 13 138 1 1.06 0.94; 7 1 19 2 0 0 1 0.989 12.56 138 1 1.06 0.94; 8 2 28 0 0 0 1 1.015 20.77 345 1 1.06 0.94; 9 1 0 0 0 0 1 1.043 28.02 345 1 1.06 0.94; 10 2 0 0 0 0 1 1.05 35.61 345 1 1.06 0.94; 11 1 70 23 0 0 1 0.985 12.72 138 1 1.06 0.94; 12 2 47 10 0 0 1 0.99 12.2 138 1 1.06 0.94; 13 1 34-0.023909986*Pwind 16 0 0 1 0.968 11.35 138 1

1.06 0.94; 14 1 14-0.009845288*Pwind 1 0 0 1 0.984 11.5 138 1

1.06 0.94; 15 2 90-0.063291139*Pwind 30 0 0 1 0.97 11.23 138 1

1.06 0.94; 16 1 25 10 0 0 1 0.984 11.91 138 1 1.06 0.94; 17 1 11 3 0 0 1 0.995 13.74 138 1 1.06 0.94; 18 2 60 34 0 0 1 0.973 11.53 138 1 1.06 0.94; 19 2 45-0.03164557*Pwind 25 0 0 1 0.963 11.05 138 1 1.06

0.94; 20 1 18-0.012658228*Pwind 3 0 0 1 0.958 11.93 138 1

1.06 0.94; 21 1 14-0.009845288*Pwind 8 0 0 1 0.959 13.52 138 1

1.06 0.94; 22 1 10 5 0 0 1 0.97 16.08 138 1 1.06 0.94; 23 1 7 3 0 0 1 1 21 138 1 1.06 0.94; 24 2 13 0 0 0 1 0.992 20.89 138 1 1.06 0.94; 25 2 0 0 0 0 1 1.05 27.93 138 1 1.06 0.94; 26 2 0 0 0 0 1 1.015 29.71 345 1 1.06 0.94; 27 2 71 13 0 0 1 0.968 15.35 138 1 1.06 0.94; 28 1 17-0.011954993*Pwind 7 0 0 1 0.962 13.62 138 1

1.06 0.94; 29 1 24-0.016877637*Pwind 4 0 0 1 0.963 12.63 138 1

1.06 0.94; 30 1 0 0 0 0 1 0.968 18.79 345 1 1.06 0.94; 31 2 43-0.030239100*Pwind 27 0 0 1 0.967 12.75 138 1

1.06 0.94; 32 2 59 23 0 0 1 0.964 14.8 138 1 1.06 0.94; 33 1 23-0.016174402*Pwind 9 0 0 1 0.972 10.63 138 1

1.06 0.94; 34 2 59 26 0 14 1 0.986 11.3 138 1 1.06 0.94; 35 1 33 9 0 0 1 0.981 10.87 138 1 1.06 0.94; 36 2 31 17 0 0 1 0.98 10.87 138 1 1.06 0.94; 37 1 0 0 0 -25 1 0.992 11.77 138 1 1.06 0.94; 38 1 0 0 0 0 1 0.962 16.91 345 1 1.06 0.94; 39 1 27-0.018987342*Pwind 11 0 0 1 0.97 8.41 138 1

1.06 0.94;

68

40 2 66-0.046413502*Pwind 23 0 0 1 0.97 7.35 138 1

1.06 0.94; 41 1 37-0.026019691*Pwind 10 0 0 1 0.967 6.92 138 1

1.06 0.94; 42 2 96-0.067510549*Pwind 23 0 0 1 0.985 8.53 138 1

1.06 0.94; 43 1 18-0.012658228*Pwind 7 0 0 1 0.978 11.28 138 1

1.06 0.94; 44 1 16-0.011251758*Pwind 8 0 10 1 0.985 13.82 138 1

1.06 0.94; 45 1 53-0.037271449*Pwind 22 0 10 1 0.987 15.67 138 1

1.06 0.94; 46 2 28 10 0 10 1 1.005 18.49 138 1 1.06 0.94; 47 1 34 0 0 0 1 1.017 20.73 138 1 1.06 0.94; 48 1 20 11 0 15 1 1.021 19.93 138 1 1.06 0.94; 49 2 87 30 0 0 1 1.025 20.94 138 1 1.06 0.94; 50 1 17 4 0 0 1 1.001 18.9 138 1 1.06 0.94; 51 1 17-0.011954993*Pwind 8 0 0 1 0.967 16.28 138 1

1.06 0.94; 52 1 18-0.012658228*Pwind 5 0 0 1 0.957 15.32 138 1

1.06 0.94; 53 1 23-0.016174402*Pwind 11 0 0 1 0.946 14.35 138 1

1.06 0.94; 54 2 113-0.079465541*Pwind 32 0 0 1 0.955 15.26 138 1

1.06 0.94; 55 2 63-0.044303797*Pwind 22 0 0 1 0.952 14.97 138 1

1.06 0.94; 56 2 84-0.05907173*Pwind 18 0 0 1 0.954 15.16 138 1 1.06

0.94; 57 1 12-0.008438819*Pwind 3 0 0 1 0.971 16.36 138 1

1.06 0.94; 58 1 12-0.008438819*Pwind 3 0 0 1 0.959 15.51 138 1

1.06 0.94; 59 2 277 113 0 0 1 0.985 19.37 138 1 1.06 0.94; 60 1 78 3 0 0 1 0.993 23.15 138 1 1.06 0.94; 61 2 0 0 0 0 1 0.995 24.04 138 1 1.06 0.94; 62 2 77 14 0 0 1 0.998 23.43 138 1 1.06 0.94; 63 1 0 0 0 0 1 0.969 22.75 345 1 1.06 0.94; 64 1 0 0 0 0 1 0.984 24.52 345 1 1.06 0.94; 65 2 0 0 0 0 1 1.005 27.65 345 1 1.06 0.94; 66 2 39 18 0 0 1 1.05 27.48 138 1 1.06 0.94; 67 1 28 7 0 0 1 1.02 24.84 138 1 1.06 0.94; 68 1 0 0 0 0 1 1.003 27.55 345 1 1.06 0.94; 69 3 0 0 0 0 1 1.035 30 138 1 1.06 0.94; 70 2 66 20 0 0 1 0.984 22.58 138 1 1.06 0.94; 71 1 0 0 0 0 1 0.987 22.15 138 1 1.06 0.94; 72 2 12 0 0 0 1 0.98 20.98 138 1 1.06 0.94; 73 2 6 0 0 0 1 0.991 21.94 138 1 1.06 0.94; 74 2 68-0.047819972*Pwind 27 0 12 1 0.958 21.64 138 1

1.06 0.94; 75 1 47 11 0 0 1 0.967 22.91 138 1 1.06 0.94; 76 2 68-0.047819972*Pwind 36 0 0 1 0.943 21.77 138 1

1.06 0.94; 77 2 61 28 0 0 1 1.006 26.72 138 1 1.06 0.94; 78 1 71 26 0 0 1 1.003 26.42 138 1 1.06 0.94; 79 1 39 32 0 20 1 1.009 26.72 138 1 1.06 0.94; 80 2 130 26 0 0 1 1.04 28.96 138 1 1.06 0.94; 81 1 0 0 0 0 1 0.997 28.1 345 1 1.06 0.94; 82 1 54 27 0 20 1 0.989 27.24 138 1 1.06 0.94;

69

83 1 20 10 0 10 1 0.985 28.42 138 1 1.06 0.94; 84 1 11 7 0 0 1 0.98 30.95 138 1 1.06 0.94; 85 2 24 15 0 0 1 0.985 32.51 138 1 1.06 0.94; 86 1 21 10 0 0 1 0.987 31.14 138 1 1.06 0.94; 87 2 0 0 0 0 1 1.015 31.4 161 1 1.06 0.94; 88 1 48 10 0 0 1 0.987 35.64 138 1 1.06 0.94; 89 2 0 0 0 0 1 1.005 39.69 138 1 1.06 0.94; 90 2 163 42 0 0 1 0.985 33.29 138 1 1.06 0.94; 91 2 10 0 0 0 1 0.98 33.31 138 1 1.06 0.94; 92 2 65 10 0 0 1 0.993 33.8 138 1 1.06 0.94; 93 1 12 7 0 0 1 0.987 30.79 138 1 1.06 0.94; 94 1 30 16 0 0 1 0.991 28.64 138 1 1.06 0.94; 95 1 42 31 0 0 1 0.981 27.67 138 1 1.06 0.94; 96 1 38 15 0 0 1 0.993 27.51 138 1 1.06 0.94; 97 1 15 9 0 0 1 1.011 27.88 138 1 1.06 0.94; 98 1 34 8 0 0 1 1.024 27.4 138 1 1.06 0.94; 99 2 42 0 0 0 1 1.01 27.04 138 1 1.06 0.94; 100 2 37 18 0 0 1 1.017 28.03 138 1 1.06 0.94; 101 1 22 15 0 0 1 0.993 29.61 138 1 1.06 0.94; 102 1 5 3 0 0 1 0.991 32.3 138 1 1.06 0.94; 103 2 23 16 0 0 1 1.001 24.44 138 1 1.06 0.94; 104 2 38 25 0 0 1 0.971 21.69 138 1 1.06 0.94; 105 2 31 26 0 20 1 0.965 20.57 138 1 1.06 0.94; 106 1 43 16 0 0 1 0.962 20.32 138 1 1.06 0.94; 107 2 50-0.035161744*Pwind 12 0 6 1 0.952 17.53 138 1

1.06 0.94; 108 1 2 1 0 0 1 0.967 19.38 138 1 1.06 0.94; 109 1 8 3 0 0 1 0.967 18.93 138 1 1.06 0.94; 110 2 39 30 0 6 1 0.973 18.09 138 1 1.06 0.94; 111 2 0 0 0 0 1 0.98 19.74 138 1 1.06 0.94; 112 2 68-0.047819972*Pwind 13 0 0 1 0.975 14.99 138 1

1.06 0.94; 113 2 6 0 0 0 1 0.993 13.74 138 1 1.06 0.94; 114 1 8-0.005625879*Pwind 3 0 0 1 0.96 14.46 138 1 1.06

0.94; 115 1 22-0.015471167*Pwind 7 0 0 1 0.96 14.46 138 1

1.06 0.94; 116 2 184 0 0 0 1 1.005 27.12 138 1 1.06 0.94; 117 1 20-0.014064698*Pwind 8 0 0 1 0.974 10.67 138 1

1.06 0.94; 118 1 33-0.023206751*Pwind 15 0 0 1 0.949 21.92 138 1

1.06 0.94;

];

Electricity Market Framework for Renewable and …...The final model used was based upon the IEEE...

Documents

Transcript of Electricity Market Framework for Renewable and …...The final model used was based upon the IEEE...